Anomalous pixel detection

ABSTRACT

Various techniques are provided to identify anomalous pixels in images captured by imaging devices. In one example, an infrared image frame is received. The infrared image frame is captured by a plurality of infrared sensors based on infrared radiation passed through an optical element. A pixel of the infrared image frame is selected. A plurality of neighborhood pixels of the infrared image frame are selected. Values of the selected pixel and the neighborhood pixels are processed to determine whether the value of the selected pixel exhibits a disparity in relation to the neighborhood pixels that exceeds a maximum disparity associated with a configuration of the optical element and the infrared sensors. The selected pixel is selectively designated as an anomalous pixel based on the processing.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Patent ApplicationNo. PCT/US2013/078554 filed Dec. 31, 2013 and entitled “ANOMALOUS PIXELDETECTION” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 claims thebenefit of U.S. Provisional Patent Application No. 61/747,844 filed Dec.31, 2012 and entitled “ANOMALOUS PIXEL DETECTION” which is herebyincorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/029,683filed Sep. 17, 2013 and entitled “PIXEL-WISE NOISE REDUCTION IN THERMALIMAGES”, which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/029,683 filed Sep. 17, 2013 and entitled “PIXEL-WISE NOISEREDUCTION IN THERMAL IMAGES”, which is hereby incorporated by referencein its entirety.

U.S. patent application Ser. No. 14/029,683 claims the benefit of U.S.Provisional Patent Application No. 61/745,489 filed Dec. 21, 2012 andentitled “ROW AND COLUMN NOISE REDUCTION IN THERMAL IMAGES”, which ishereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,683 claims the benefit of U.S.Provisional Patent Application No. 61/745,504 filed Dec. 21, 2012 andentitled “PIXEL-WISE NOISE REDUCTION IN THERMAL IMAGES”, which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,683 is a continuation-in-part ofU.S. patent application Ser. No. 13/622,178 filed Sep. 18, 2012 andentitled “SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES”, which isa continuation-in-part of U.S. patent application Ser. No. 13/529,772filed Jun. 21, 2012 and entitled “SYSTEMS AND METHODS FOR PROCESSINGINFRARED IMAGES”, which is a continuation of U.S. patent applicationSer. No. 12/396,340 filed Mar. 2, 2009 and entitled “SYSTEMS AND METHODSFOR PROCESSING INFRARED IMAGES”, all of which are hereby incorporated byreference in their entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/029,716filed Sep. 17, 2013 and entitled “ROW AND COLUMN NOISE REDUCTION INTHERMAL IMAGES”, which is hereby incorporated by reference in itsentirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/029,716 filed Sep. 17, 2013 and entitled “ROW AND COLUMNNOISE REDUCTION IN THERMAL IMAGES”, which is hereby incorporated byreference in its entirety.

U.S. patent application Ser. No. 14/029,716 claims the benefit of U.S.Provisional Patent Application No. 61/745,489 filed Dec. 21, 2012 andentitled “ROW AND COLUMN NOISE REDUCTION IN THERMAL IMAGES”, which ishereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,716 claims the benefit of U.S.Provisional Patent Application No. 61/745,504 filed Dec. 21, 2012 andentitled “PIXEL-WISE NOISE REDUCTION IN THERMAL IMAGES”, which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,716 a continuation-in-part ofU.S. patent application Ser. No. 13/622,178 filed Sep. 18, 2012 andentitled “SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES”, which isa continuation-in-part of U.S. patent application Ser. No. 13/529,772filed Jun. 21, 2012 and entitled “SYSTEMS AND METHODS FOR PROCESSINGINFRARED IMAGES”, which is a continuation of U.S. patent applicationSer. No. 12/396,340 filed Mar. 2, 2009 and entitled “SYSTEMS AND METHODSFOR PROCESSING INFRARED IMAGES”, all of which are hereby incorporated byreference in their entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/101,245filed Dec. 9, 2013 and entitled “LOW POWER AND SMALL FORM FACTORINFRARED IMAGING” which is hereby incorporated by reference in itsentirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/101,245 filed Dec. 9, 2013 and entitled “LOW POWER AND SMALLFORM FACTOR INFRARED IMAGING” which is hereby incorporated by referencein its entirety.

U.S. patent application Ser. No. 14/101,245 is a continuation ofInternational Patent Application No. PCT/US2012/041744 filed Jun. 8,2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/656,889 filed Jun.7, 2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct.7, 2011 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/099,818filed Dec. 6, 2013 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUESFOR INFRARED IMAGING DEVICES” which is hereby incorporated by referencein its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/099,818 filed Dec. 6, 2013 and entitled “NON-UNIFORMITYCORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES” which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/099,818 is a continuation ofInternational Patent Application No. PCT/US2012/041749 filed Jun. 8,2012 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct.7, 2011 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/101,258filed Dec. 9, 2013 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES”which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/101,258 filed Dec. 9, 2013 and entitled “INFRARED CAMERASYSTEM ARCHITECTURES” which is hereby incorporated by reference in itsentirety.

U.S. patent application Ser. No. 14/101,258 is a continuation ofInternational Patent Application No. PCT/US2012/041739 filed Jun. 8,2012 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is herebyincorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/138,058filed Dec. 21, 2013 and entitled “COMPACT MULTI-SPECTRUM IMAGING WITHFUSION” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/138,058 filed Dec. 21, 2013 and entitled “COMPACTMULTI-SPECTRUM IMAGING WITH FUSION” which is hereby incorporated byreference in its entirety.

U.S. patent application Ser. No. 14/138,058 claims the benefit of U.S.Provisional Patent Application No. 61/748,018 filed Dec. 31, 2012 andentitled “COMPACT MULTI-SPECTRUM IMAGING WITH FUSION” which is herebyincorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/138,040filed Dec. 21, 2013 and entitled “TIME SPACED INFRARED IMAGEENHANCEMENT” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/138,040 filed Dec. 21, 2013 and entitled “TIME SPACEDINFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference inits entirety.

U.S. patent application Ser. No. 14/138,040 claims the benefit of U.S.Provisional Patent Application No. 61/792,582 filed Mar. 15, 2013 andentitled “TIME SPACED INFRARED IMAGE ENHANCEMENT” which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,040 also claims the benefit ofU.S. Provisional Patent Application No. 61/746,069 filed Dec. 26, 2012and entitled “TIME SPACED INFRARED IMAGE ENHANCEMENT” which is herebyincorporated by reference in its entirety.

International Patent Application No. PCT/US2013/078554 is acontinuation-in-part of U.S. patent application Ser. No. 14/138,052filed Dec. 21, 2013 and entitled “INFRARED IMAGING ENHANCEMENT WITHFUSION” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/138,052 filed Dec. 21, 2013 and entitled “INFRARED IMAGINGENHANCEMENT WITH FUSION” which is hereby incorporated by reference inits entirety.

U.S. patent application Ser. No. 14/138,052 claims the benefit of U.S.Provisional Patent Application No. 61/793,952 filed Mar. 15, 2013 andentitled “INFRARED IMAGING ENHANCEMENT WITH FUSION” which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,052 also claims the benefit ofU.S. Provisional Patent Application No. 61/746,074 filed Dec. 26, 2012and entitled “INFRARED IMAGING ENHANCEMENT WITH FUSION” which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

One or more embodiments of the invention relate generally to imagingdevices and more particularly, for example, to detection of anomalouspixels in images.

BACKGROUND

Digital images include a plurality of pixels arranged in rows andcolumns. For example, each individual pixel may be associated with asensor, such as an infrared sensor (e.g., a microbolometer), a visiblespectrum sensor, and/or other appropriate sensing element.

Failures and/or defects in such sensors or other components of animaging device may result in one or more individual pixels or groups ofpixels exhibiting anomalous behavior (e.g., “bad pixels”). Anomalouspixels can be especially problematic for imaging devices with smallarray sizes (e.g., having correspondingly small numbers of pixels), aseach pixel may have a proportionally larger contribution to the overallimage than in large array sizes.

Conventional quality control techniques typically include human and/ormachine-based evaluation of captured images to identify anomalous pixelsbefore imaging devices are shipped from the factory. However,conventional techniques may not always identify anomalous pixelsreliably, especially in the case of intermittent operation. In addition,human-based evaluation may not be practical or cost effective for largevolume manufacturing.

SUMMARY

Various techniques are provided to identify anomalous pixels in imagescaptured by imaging devices. In one embodiment, a method includesreceiving an infrared image frame captured by a plurality of infraredsensors based on infrared radiation passed through an optical element;selecting a pixel of the infrared image frame; selecting a plurality ofneighborhood pixels of the infrared image frame; processing values ofthe selected pixel and the neighborhood pixels to determine whether thevalue of the selected pixel exhibits a disparity in relation to theneighborhood pixels that exceeds a maximum disparity associated with aconfiguration of the optical element and the infrared sensors; andselectively designating the selected pixel as an anomalous pixel basedon the processing.

In another embodiment, a system includes a memory adapted to receive aninfrared image frame captured by a plurality of infrared sensors basedon infrared radiation passed through an optical element; and a processoradapted to execute instructions to: select a pixel of the infrared imageframe, select a plurality of neighborhood pixels of the infrared imageframe, process values of the selected pixel and the neighborhood pixelsto determine whether the value of the selected pixel exhibits adisparity in relation to the neighborhood pixels that exceeds a maximumdisparity associated with a configuration of the optical element and theinfrared sensors, and selectively designate the selected pixel as ananomalous pixel based on the process.

The scope of the invention is defined by the claims, which areincorporated into this section by reference. A more completeunderstanding of embodiments of the invention will be afforded to thoseskilled in the art, as well as a realization of additional advantagesthereof, by a consideration of the following detailed description of oneor more embodiments. Reference will be made to the appended sheets ofdrawings that will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an infrared imaging module configured to beimplemented in a host device in accordance with an embodiment of thedisclosure.

FIG. 2 illustrates an assembled infrared imaging module in accordancewith an embodiment of the disclosure.

FIG. 3 illustrates an exploded view of an infrared imaging modulejuxtaposed over a socket in accordance with an embodiment of thedisclosure.

FIG. 4 illustrates a block diagram of an infrared sensor assemblyincluding an array of infrared sensors in accordance with an embodimentof the disclosure.

FIG. 5 illustrates a flow diagram of various operations to determinenon-uniformity correction (NUC) terms in accordance with an embodimentof the disclosure.

FIG. 6 illustrates differences between neighboring pixels in accordancewith an embodiment of the disclosure.

FIG. 7 illustrates a flat field correction technique in accordance withan embodiment of the disclosure.

FIG. 8 illustrates various image processing techniques of FIG. 5 andother operations applied in an image processing pipeline in accordancewith an embodiment of the disclosure.

FIG. 9 illustrates a temporal noise reduction process in accordance withan embodiment of the disclosure.

FIG. 10 illustrates particular implementation details of severalprocesses of the image processing pipeline of FIG. 8 in accordance withan embodiment of the disclosure.

FIG. 11 illustrates spatially correlated fixed pattern noise (FPN) in aneighborhood of pixels in accordance with an embodiment of thedisclosure.

FIG. 12 illustrates a block diagram of another implementation of aninfrared sensor assembly including an array of infrared sensors and alow-dropout regulator in accordance with an embodiment of thedisclosure.

FIG. 13 illustrates a circuit diagram of a portion of the infraredsensor assembly of FIG. 12 in accordance with an embodiment of thedisclosure.

FIG. 14 shows a block diagram of a system for infrared image processingin accordance with an embodiment of the disclosure.

FIGS. 15A-C are flowcharts illustrating methods for noise filtering aninfrared image in accordance with embodiments of the disclosure.

FIGS. 16A-C are graphs illustrating infrared image data and theprocessing of an infrared image in accordance with embodiments of thedisclosure.

FIG. 17 shows a portion of a row of sensor data for discussingprocessing techniques in accordance with embodiments of the disclosure.

FIGS. 18A-C show an exemplary implementation of column and row noisefiltering for an infrared image in accordance with embodiments of thedisclosure.

FIG. 19A shows an infrared image of a scene including small verticalstructure in accordance with an embodiment of the disclosure.

FIG. 19B shows a corrected version of the infrared image of FIG. 19A inaccordance with an embodiment of the disclosure.

FIG. 20A shows an infrared image of a scene including a large verticalstructure in accordance with an embodiment of the disclosure.

FIG. 20B shows a corrected version of the infrared image of FIG. 20A inaccordance with an embodiment of the disclosure.

FIG. 21 is a flowchart illustrating another method for noise filteringan infrared image in accordance with an embodiment of the disclosure.

FIG. 22A shows a histogram prepared for the infrared image of FIG. 19Ain accordance with an embodiment of the disclosure.

FIG. 22B shows a histogram prepared for the infrared image of FIG. 20Ain accordance with an embodiment of the disclosure.

FIG. 23A illustrates an infrared image of a scene in accordance with anembodiment of the disclosure.

FIG. 23B is a flowchart illustrating still another method for noisefiltering an infrared image in accordance with an embodiment of thedisclosure.

FIGS. 23C-E show histograms prepared for neighborhoods around selectedpixels of the infrared image of FIG. 23A in accordance with embodimentsof the disclosure.

FIG. 24 illustrates an Airy disk and a corresponding plot of itsintensity versus location on a focal plane array (FPA) in accordancewith an embodiment of the disclosure.

FIG. 25 illustrates a technique to identify anomalous pixels inaccordance with an embodiment of the disclosure.

FIG. 26 illustrates another technique to identify anomalous pixels inaccordance with an embodiment of the disclosure.

FIG. 27 is a flowchart illustrating a process to identify anomalouspixels in accordance with an embodiment of the disclosure.

Embodiments of the invention and their advantages are best understood byreferring to the detailed description that follows. It should beappreciated that like reference numerals are used to identify likeelements illustrated in one or more of the figures.

DETAILED DESCRIPTION

FIG. 1 illustrates an infrared imaging module 100 (e.g., an infraredcamera or an infrared imaging device) configured to be implemented in ahost device 102 in accordance with an embodiment of the disclosure.Infrared imaging module 100 may be implemented, for one or moreembodiments, with a small form factor and in accordance with wafer levelpackaging techniques or other packaging techniques.

In one embodiment, infrared imaging module 100 may be configured to beimplemented in a small portable host device 102, such as a mobiletelephone, a tablet computing device, a laptop computing device, apersonal digital assistant, a visible light camera, a music player, orany other appropriate mobile device. In this regard, infrared imagingmodule 100 may be used to provide infrared imaging features to hostdevice 102. For example, infrared imaging module 100 may be configuredto capture, process, and/or otherwise manage infrared images (e.g., alsoreferred to as image frames) and provide such infrared images to hostdevice 102 for use in any desired fashion (e.g., for further processing,to store in memory, to display, to use by various applications runningon host device 102, to export to other devices, or other uses).

In various embodiments, infrared imaging module 100 may be configured tooperate at low voltage levels and over a wide temperature range. Forexample, in one embodiment, infrared imaging module 100 may operateusing a power supply of approximately 2.4 volts, 2.5 volts, 2.8 volts,or lower voltages, and operate over a temperature range of approximately−20 degrees C. to approximately +60 degrees C. (e.g., providing asuitable dynamic range and performance over an environmental temperaturerange of approximately 80 degrees C.). In one embodiment, by operatinginfrared imaging module 100 at low voltage levels, infrared imagingmodule 100 may experience reduced amounts of self heating in comparisonwith other types of infrared imaging devices. As a result, infraredimaging module 100 may be operated with reduced measures to compensatefor such self heating.

As shown in FIG. 1, host device 102 may include a socket 104, a shutter105, motion sensors 194, a processor 195, a memory 196, a display 197,and/or other components 198. Socket 104 may be configured to receiveinfrared imaging module 100 as identified by arrow 101. In this regard,FIG. 2 illustrates infrared imaging module 100 assembled in socket 104in accordance with an embodiment of the disclosure.

Motion sensors 194 may be implemented by one or more accelerometers,gyroscopes, or other appropriate devices that may be used to detectmovement of host device 102. Motion sensors 194 may be monitored by andprovide information to processing module 160 or processor 195 to detectmotion. In various embodiments, motion sensors 194 may be implemented aspart of host device 102 (as shown in FIG. 1), infrared imaging module100, or other devices attached to or otherwise interfaced with hostdevice 102.

Processor 195 may be implemented as any appropriate processing device(e.g., logic device, microcontroller, processor, application specificintegrated circuit (ASIC), or other device) that may be used by hostdevice 102 to execute appropriate instructions, such as softwareinstructions provided in memory 196. Display 197 may be used to displaycaptured and/or processed infrared images and/or other images, data, andinformation. Other components 198 may be used to implement any featuresof host device 102 as may be desired for various applications (e.g.,clocks, temperature sensors, a visible light camera, or othercomponents). In addition, a machine readable medium 193 may be providedfor storing non-transitory instructions for loading into memory 196 andexecution by processor 195.

In various embodiments, infrared imaging module 100 and socket 104 maybe implemented for mass production to facilitate high volumeapplications, such as for implementation in mobile telephones or otherdevices (e.g., requiring small form factors). In one embodiment, thecombination of infrared imaging module 100 and socket 104 may exhibitoverall dimensions of approximately 8.5 mm by 8.5 mm by 5.9 mm whileinfrared imaging module 100 is installed in socket 104.

FIG. 3 illustrates an exploded view of infrared imaging module 100juxtaposed over socket 104 in accordance with an embodiment of thedisclosure. Infrared imaging module 100 may include a lens barrel 110, ahousing 120, an infrared sensor assembly 128, a circuit board 170, abase 150, and a processing module 160.

Lens barrel 110 may at least partially enclose an optical element 180(e.g., a lens) which is partially visible in FIG. 3 through an aperture112 in lens barrel 110. Lens barrel 110 may include a substantiallycylindrical extension 114 which may be used to interface lens barrel 110with an aperture 122 in housing 120.

Infrared sensor assembly 128 may be implemented, for example, with a cap130 (e.g., a lid) mounted on a substrate 140. Infrared sensor assembly128 may include a plurality of infrared sensors 132 (e.g., infrareddetectors) implemented in an array or other fashion on substrate 140 andcovered by cap 130. For example, in one embodiment, infrared sensorassembly 128 may be implemented as a focal plane array (FPA). Such afocal plane array may be implemented, for example, as a vacuum packageassembly (e.g., sealed by cap 130 and substrate 140). In one embodiment,infrared sensor assembly 128 may be implemented as a wafer level package(e.g., infrared sensor assembly 128 may be singulated from a set ofvacuum package assemblies provided on a wafer). In one embodiment,infrared sensor assembly 128 may be implemented to operate using a powersupply of approximately 2.4 volts, 2.5 volts, 2.8 volts, or similarvoltages.

Infrared sensors 132 may be configured to detect infrared radiation(e.g., infrared energy) from a target scene including, for example, midwave infrared wave bands (MWIR), long wave infrared wave bands (LWIR),and/or other thermal imaging bands as may be desired in particularimplementations. In one embodiment, infrared sensor assembly 128 may beprovided in accordance with wafer level packaging techniques.

Infrared sensors 132 may be implemented, for example, as microbolometersor other types of thermal imaging infrared sensors arranged in anydesired array pattern to provide a plurality of pixels. In oneembodiment, infrared sensors 132 may be implemented as vanadium oxide(VOx) detectors with a 17 μm pixel pitch. In various embodiments, arraysof approximately 32 by 32 infrared sensors 132, approximately 64 by 64infrared sensors 132, approximately 80 by 64 infrared sensors 132, orother array sizes may be used.

Substrate 140 may include various circuitry including, for example, aread out integrated circuit (ROIC) with dimensions less thanapproximately 5.5 mm by 5.5 mm in one embodiment. Substrate 140 may alsoinclude bond pads 142 that may be used to contact complementaryconnections positioned on inside surfaces of housing 120 when infraredimaging module 100 is assembled as shown in FIG. 3. In one embodiment,the ROIC may be implemented with low-dropout regulators (LDO) to performvoltage regulation to reduce power supply noise introduced to infraredsensor assembly 128 and thus provide an improved power supply rejectionratio (PSRR). Moreover, by implementing the LDO with the ROIC (e.g.,within a wafer level package), less die area may be consumed and fewerdiscrete die (or chips) are needed.

FIG. 4 illustrates a block diagram of infrared sensor assembly 128including an array of infrared sensors 132 in accordance with anembodiment of the disclosure. In the illustrated embodiment, infraredsensors 132 are provided as part of a unit cell array of a ROIC 402.ROIC 402 includes bias generation and timing control circuitry 404,column amplifiers 405, a column multiplexer 406, a row multiplexer 408,and an output amplifier 410. Image frames (e.g., thermal images)captured by infrared sensors 132 may be provided by output amplifier 410to processing module 160, processor 195, and/or any other appropriatecomponents to perform various processing techniques described herein.Although an 8 by 8 array is shown in FIG. 4, any desired arrayconfiguration may be used in other embodiments. Further descriptions ofROICs and infrared sensors (e.g., microbolometer circuits) may be foundin U.S. Pat. No. 6,028,309 issued Feb. 22, 2000, which is incorporatedherein by reference in its entirety.

Infrared sensor assembly 128 may capture images (e.g., image frames) andprovide such images from its ROIC at various rates. Processing module160 may be used to perform appropriate processing of captured infraredimages and may be implemented in accordance with any appropriatearchitecture. In one embodiment, processing module 160 may beimplemented as an ASIC. In this regard, such an ASIC may be configuredto perform image processing with high performance and/or highefficiency. In another embodiment, processing module 160 may beimplemented with a general purpose central processing unit (CPU) whichmay be configured to execute appropriate software instructions toperform image processing, coordinate and perform image processing withvarious image processing blocks, coordinate interfacing betweenprocessing module 160 and host device 102, and/or other operations. Inyet another embodiment, processing module 160 may be implemented with afield programmable gate array (FPGA). Processing module 160 may beimplemented with other types of processing and/or logic circuits inother embodiments as would be understood by one skilled in the art.

In these and other embodiments, processing module 160 may also beimplemented with other components where appropriate, such as, volatilememory, non-volatile memory, and/or one or more interfaces (e.g.,infrared detector interfaces, inter-integrated circuit (I2C) interfaces,mobile industry processor interfaces (MIPI), joint test action group(JTAG) interfaces (e.g., IEEE 1149.1 standard test access port andboundary-scan architecture), and/or other interfaces).

In some embodiments, infrared imaging module 100 may further include oneor more actuators 199 which may be used to adjust the focus of infraredimage frames captured by infrared sensor assembly 128. For example,actuators 199 may be used to move optical element 180, infrared sensors132, and/or other components relative to each other to selectively focusand defocus infrared image frames in accordance with techniquesdescribed herein. Actuators 199 may be implemented in accordance withany type of motion-inducing apparatus or mechanism, and may positionedat any location within or external to infrared imaging module 100 asappropriate for different applications.

When infrared imaging module 100 is assembled, housing 120 maysubstantially enclose infrared sensor assembly 128, base 150, andprocessing module 160. Housing 120 may facilitate connection of variouscomponents of infrared imaging module 100. For example, in oneembodiment, housing 120 may provide electrical connections 126 toconnect various components as further described.

Electrical connections 126 (e.g., conductive electrical paths, traces,or other types of connections) may be electrically connected with bondpads 142 when infrared imaging module 100 is assembled. In variousembodiments, electrical connections 126 may be embedded in housing 120,provided on inside surfaces of housing 120, and/or otherwise provided byhousing 120. Electrical connections 126 may terminate in connections 124protruding from the bottom surface of housing 120 as shown in FIG. 3.Connections 124 may connect with circuit board 170 when infrared imagingmodule 100 is assembled (e.g., housing 120 may rest atop circuit board170 in various embodiments). Processing module 160 may be electricallyconnected with circuit board 170 through appropriate electricalconnections. As a result, infrared sensor assembly 128 may beelectrically connected with processing module 160 through, for example,conductive electrical paths provided by: bond pads 142, complementaryconnections on inside surfaces of housing 120, electrical connections126 of housing 120, connections 124, and circuit board 170.Advantageously, such an arrangement may be implemented without requiringwire bonds to be provided between infrared sensor assembly 128 andprocessing module 160.

In various embodiments, electrical connections 126 in housing 120 may bemade from any desired material (e.g., copper or any other appropriateconductive material). In one embodiment, electrical connections 126 mayaid in dissipating heat from infrared imaging module 100.

Other connections may be used in other embodiments. For example, in oneembodiment, sensor assembly 128 may be attached to processing module 160through a ceramic board that connects to sensor assembly 128 by wirebonds and to processing module 160 by a ball grid array (BGA). Inanother embodiment, sensor assembly 128 may be mounted directly on arigid flexible board and electrically connected with wire bonds, andprocessing module 160 may be mounted and connected to the rigid flexibleboard with wire bonds or a BGA.

The various implementations of infrared imaging module 100 and hostdevice 102 set forth herein are provided for purposes of example, ratherthan limitation. In this regard, any of the various techniques describedherein may be applied to any infrared camera system, infrared imager, orother device for performing infrared/thermal imaging.

Substrate 140 of infrared sensor assembly 128 may be mounted on base150. In various embodiments, base 150 (e.g., a pedestal) may be made,for example, of copper formed by metal injection molding (MIM) andprovided with a black oxide or nickel-coated finish. In variousembodiments, base 150 may be made of any desired material, such as forexample zinc, aluminum, or magnesium, as desired for a given applicationand may be formed by any desired applicable process, such as for examplealuminum casting, MIM, or zinc rapid casting, as may be desired forparticular applications. In various embodiments, base 150 may beimplemented to provide structural support, various circuit paths,thermal heat sink properties, and other features where appropriate. Inone embodiment, base 150 may be a multi-layer structure implemented atleast in part using ceramic material.

In various embodiments, circuit board 170 may receive housing 120 andthus may physically support the various components of infrared imagingmodule 100. In various embodiments, circuit board 170 may be implementedas a printed circuit board (e.g., an FR4 circuit board or other types ofcircuit boards), a rigid or flexible interconnect (e.g., tape or othertype of interconnects), a flexible circuit substrate, a flexible plasticsubstrate, or other appropriate structures. In various embodiments, base150 may be implemented with the various features and attributesdescribed for circuit board 170, and vice versa.

Socket 104 may include a cavity 106 configured to receive infraredimaging module 100 (e.g., as shown in the assembled view of FIG. 2).Infrared imaging module 100 and/or socket 104 may include appropriatetabs, arms, pins, fasteners, or any other appropriate engagement memberswhich may be used to secure infrared imaging module 100 to or withinsocket 104 using friction, tension, adhesion, and/or any otherappropriate manner. Socket 104 may include engagement members 107 thatmay engage surfaces 109 of housing 120 when infrared imaging module 100is inserted into a cavity 106 of socket 104. Other types of engagementmembers may be used in other embodiments.

Infrared imaging module 100 may be electrically connected with socket104 through appropriate electrical connections (e.g., contacts, pins,wires, or any other appropriate connections). For example, socket 104may include electrical connections 108 which may contact correspondingelectrical connections of infrared imaging module 100 (e.g.,interconnect pads, contacts, or other electrical connections on side orbottom surfaces of circuit board 170, bond pads 142 or other electricalconnections on base 150, or other connections). Electrical connections108 may be made from any desired material (e.g., copper or any otherappropriate conductive material). In one embodiment, electricalconnections 108 may be mechanically biased to press against electricalconnections of infrared imaging module 100 when infrared imaging module100 is inserted into cavity 106 of socket 104. In one embodiment,electrical connections 108 may at least partially secure infraredimaging module 100 in socket 104. Other types of electrical connectionsmay be used in other embodiments.

Socket 104 may be electrically connected with host device 102 throughsimilar types of electrical connections. For example, in one embodiment,host device 102 may include electrical connections (e.g., solderedconnections, snap-in connections, or other connections) that connectwith electrical connections 108 passing through apertures 190. Invarious embodiments, such electrical connections may be made to thesides and/or bottom of socket 104.

Various components of infrared imaging module 100 may be implementedwith flip chip technology which may be used to mount components directlyto circuit boards without the additional clearances typically needed forwire bond connections. Flip chip connections may be used, as an example,to reduce the overall size of infrared imaging module 100 for use incompact small form factor applications. For example, in one embodiment,processing module 160 may be mounted to circuit board 170 using flipchip connections. For example, infrared imaging module 100 may beimplemented with such flip chip configurations.

In various embodiments, infrared imaging module 100 and/or associatedcomponents may be implemented in accordance with various techniques(e.g., wafer level packaging techniques) as set forth in U.S. patentapplication Ser. No. 12/844,124 filed Jul. 27, 2010, and U.S.Provisional Patent Application No. 61/469,651 filed Mar. 30, 2011, whichare incorporated herein by reference in their entirety. Furthermore, inaccordance with one or more embodiments, infrared imaging module 100and/or associated components may be implemented, calibrated, tested,and/or used in accordance with various techniques, such as for exampleas set forth in U.S. Pat. No. 7,470,902 issued Dec. 30, 2008, U.S. Pat.No. 6,028,309 issued Feb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov.2, 2004, U.S. Pat. No. 7,034,301 issued Apr. 25, 2006, U.S. Pat. No.7,679,048 issued Mar. 16, 2010, U.S. Pat. No. 7,470,904 issued Dec. 30,2008, U.S. patent application Ser. No. 12/202,880 filed Sep. 2, 2008,and U.S. patent application Ser. No. 12/202,896 filed Sep. 2, 2008,which are incorporated herein by reference in their entirety.

In some embodiments, host device 102 may include other components 198such as a non-thermal camera (e.g., a visible light camera or other typeof non-thermal imager). The non-thermal camera may be a small formfactor imaging module or imaging device, and may, in some embodiments,be implemented in a manner similar to the various embodiments ofinfrared imaging module 100 disclosed herein, with one or more sensorsand/or sensor arrays responsive to radiation in non-thermal spectrums(e.g., radiation in visible light wavelengths, ultraviolet wavelengths,and/or other non-thermal wavelengths). For example, in some embodiments,the non-thermal camera may be implemented with a charge-coupled device(CCD) sensor, an electron multiplying CCD (EMCCD) sensor, acomplementary metal-oxide-semiconductor (CMOS) sensor, a scientific CMOS(sCMOS) sensor, or other filters and/or sensors.

In some embodiments, the non-thermal camera may be co-located withinfrared imaging module 100 and oriented such that a field-of-view (FOV)of the non-thermal camera at least partially overlaps a FOV of infraredimaging module 100. In one example, infrared imaging module 100 and anon-thermal camera may be implemented as a dual sensor module sharing acommon substrate according to various techniques described in U.S.Provisional Patent Application No. 61/748,018 filed Dec. 31, 2012, whichis incorporated herein by reference.

For embodiments having such a non-thermal light camera, variouscomponents (e.g., processor 195, processing module 160, and/or otherprocessing component) may be configured to superimpose, fuse, blend, orotherwise combine infrared images (e.g., including thermal images)captured by infrared imaging module 100 and non-thermal images (e.g.,including visible light images) captured by a non-thermal camera,whether captured at substantially the same time or different times(e.g., time-spaced over hours, days, daytime versus nighttime, and/orotherwise).

In some embodiments, thermal and non-thermal images may be processed togenerate combined images (e.g., one or more processes performed on suchimages in some embodiments). For example, scene-based NUC processing maybe performed (as further described herein), true color processing may beperformed, and/or high contrast processing may be performed.

Regarding true color processing, thermal images may be blended withnon-thermal images by, for example, blending a radiometric component ofa thermal image with a corresponding component of a non-thermal imageaccording to a blending parameter, which may be adjustable by a userand/or machine in some embodiments. For example, luminance orchrominance components of the thermal and non-thermal images may becombined according to the blending parameter. In one embodiment, suchblending techniques may be referred to as true color infrared imagery.For example, in daytime imaging, a blended image may comprise anon-thermal color image, which includes a luminance component and achrominance component, with its luminance value replaced and/or blendedwith the luminance value from a thermal image. The use of the luminancedata from the thermal image causes the intensity of the true non-thermalcolor image to brighten or dim based on the temperature of the object.As such, these blending techniques provide thermal imaging for daytimeor visible light images.

Regarding high contrast processing, high spatial frequency content maybe obtained from one or more of the thermal and non-thermal images(e.g., by performing high pass filtering, difference imaging, and/orother techniques). A combined image may include a radiometric componentof a thermal image and a blended component including infrared (e.g.,thermal) characteristics of a scene blended with the high spatialfrequency content, according to a blending parameter, which may beadjustable by a user and/or machine in some embodiments. In someembodiments, high spatial frequency content from non-thermal images maybe blended with thermal images by superimposing the high spatialfrequency content onto the thermal images, where the high spatialfrequency content replaces or overwrites those portions of the thermalimages corresponding to where the high spatial frequency content exists.For example, the high spatial frequency content may include edges ofobjects depicted in images of a scene, but may not exist within theinterior of such objects. In such embodiments, blended image data maysimply include the high spatial frequency content, which maysubsequently be encoded into one or more components of combined images.

For example, a radiometric component of thermal image may be achrominance component of the thermal image, and the high spatialfrequency content may be derived from the luminance and/or chrominancecomponents of a non-thermal image. In this embodiment, a combined imagemay include the radiometric component (e.g., the chrominance componentof the thermal image) encoded into a chrominance component of thecombined image and the high spatial frequency content directly encoded(e.g., as blended image data but with no thermal image contribution)into a luminance component of the combined image. By doing so, aradiometric calibration of the radiometric component of the thermalimage may be retained. In similar embodiments, blended image data mayinclude the high spatial frequency content added to a luminancecomponent of the thermal images, and the resulting blended data encodedinto a luminance component of resulting combined images.

For example, any of the techniques disclosed in the followingapplications may be used in various embodiments: U.S. patent applicationSer. No. 12/477,828 filed Jun. 3, 2009; U.S. patent application Ser. No.12/766,739 filed Apr. 23, 2010; U.S. patent application Ser. No.13/105,765 filed May 11, 2011; U.S. patent application Ser. No.13/437,645 filed Apr. 2, 2012; U.S. Provisional Patent Application No.61/473,207 filed Apr. 8, 2011; U.S. Provisional Patent Application No.61/746,069 filed Dec. 26, 2012; U.S. Provisional Patent Application No.61/746,074 filed Dec. 26, 2012; U.S. Provisional Patent Application No.61/748,018 filed Dec. 31, 2012; U.S. Provisional Patent Application No.61/792,582 filed Mar. 15, 2013; U.S. Provisional Patent Application No.61/793,952 filed Mar. 15, 2013; International Patent Application No.PCT/EP2011/056432 filed Apr. 21, 2011; U.S. patent application Ser. No.14/138,040 filed Dec. 21, 2013; U.S. patent application Ser. No.14/138,052 filed Dec. 21, 2013; U.S. patent application Ser. No.14/138,058 filed Dec. 21, 2013; U.S. patent application Ser. No.14/101,245 filed Dec. 9, 2013; U.S. patent application Ser. No.14/101,258 filed Dec. 9, 2013; U.S. patent application Ser. No.14/099,818 filed Dec. 6, 2013; U.S. patent application Ser. No.14/029,683 filed Sep. 17, 2013; U.S. patent application Ser. No.14/029,716 filed Sep. 17, 2013; U.S. Provisional Patent Application No.61/745,489 filed Dec. 21, 2012; U.S. Provisional Patent Application No.61/745,504 filed Dec. 21, 2012; U.S. patent application Ser. No.13/622,178 filed Sep. 18, 2012; U.S. patent application Ser. No.13/529,772 filed Jun. 21, 2012; and U.S. patent application Ser. No.12/396,340 filed Mar. 2, 2009, all of such applications are incorporatedherein by reference in their entirety. Any of the techniques describedherein, or described in other applications or patents referenced herein,may be applied to any of the various thermal devices, non-thermaldevices, and uses described herein.

Referring again to FIG. 1, in various embodiments, host device 102 mayinclude shutter 105. In this regard, shutter 105 may be selectivelypositioned over socket 104 (e.g., as identified by arrows 103) whileinfrared imaging module 100 is installed therein. In this regard,shutter 105 may be used, for example, to protect infrared imaging module100 when not in use. Shutter 105 may also be used as a temperaturereference as part of a calibration process (e.g., a NUC process or othercalibration processes) for infrared imaging module 100 as would beunderstood by one skilled in the art.

In various embodiments, shutter 105 may be made from various materialssuch as, for example, polymers, glass, aluminum (e.g., painted oranodized) or other materials. In various embodiments, shutter 105 mayinclude one or more coatings to selectively filter electromagneticradiation and/or adjust various optical properties of shutter 105 (e.g.,a uniform blackbody coating or a reflective gold coating).

In another embodiment, shutter 105 may be fixed in place to protectinfrared imaging module 100 at all times. In this case, shutter 105 or aportion of shutter 105 may be made from appropriate materials (e.g.,polymers or infrared transmitting materials such as silicon, germanium,zinc selenide, or chalcogenide glasses) that do not substantially filterdesired infrared wavelengths. In another embodiment, a shutter may beimplemented as part of infrared imaging module 100 (e.g., within or aspart of a lens barrel or other components of infrared imaging module100), as would be understood by one skilled in the art.

Alternatively, in another embodiment, a shutter (e.g., shutter 105 orother type of external or internal shutter) need not be provided, butrather a NUC process or other type of calibration may be performed usingshutterless techniques. In another embodiment, a NUC process or othertype of calibration using shutterless techniques may be performed incombination with shutter-based techniques.

Infrared imaging module 100 and host device 102 may be implemented inaccordance with any of the various techniques set forth in U.S.Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011, U.S.Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011, andU.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011,which are incorporated herein by reference in their entirety.

In various embodiments, the components of host device 102 and/orinfrared imaging module 100 may be implemented as a local or distributedsystem with components in communication with each other over wiredand/or wireless networks. Accordingly, the various operations identifiedin this disclosure may be performed by local and/or remote components asmay be desired in particular implementations.

FIG. 5 illustrates a flow diagram of various operations to determine NUCterms in accordance with an embodiment of the disclosure. In someembodiments, the operations of FIG. 5 may be performed by processingmodule 160 or processor 195 (both also generally referred to as aprocessor) operating on image frames captured by infrared sensors 132.

In block 505, infrared sensors 132 begin capturing image frames of ascene. Typically, the scene will be the real world environment in whichhost device 102 is currently located. In this regard, shutter 105 (ifoptionally provided) may be opened to permit infrared imaging module toreceive infrared radiation from the scene. Infrared sensors 132 maycontinue capturing image frames during all operations shown in FIG. 5.In this regard, the continuously captured image frames may be used forvarious operations as further discussed. In one embodiment, the capturedimage frames may be temporally filtered (e.g., in accordance with theprocess of block 826 further described herein with regard to FIG. 8) andbe processed by other terms (e.g., factory gain terms 812, factoryoffset terms 816, previously determined NUC terms 817, column FPN terms820, and row FPN terms 824 as further described herein with regard toFIG. 8) before they are used in the operations shown in FIG. 5.

In block 510, a NUC process initiating event is detected. In oneembodiment, the NUC process may be initiated in response to physicalmovement of host device 102. Such movement may be detected, for example,by motion sensors 194 which may be polled by a processor. In oneexample, a user may move host device 102 in a particular manner, such asby intentionally waving host device 102 back and forth in an “erase” or“swipe” movement. In this regard, the user may move host device 102 inaccordance with a predetermined speed and direction (velocity), such asin an up and down, side to side, or other pattern to initiate the NUCprocess. In this example, the use of such movements may permit the userto intuitively operate host device 102 to simulate the “erasing” ofnoise in captured image frames.

In another example, a NUC process may be initiated by host device 102 ifmotion exceeding a threshold value is detected (e.g., motion greaterthan expected for ordinary use). It is contemplated that any desiredtype of spatial translation of host device 102 may be used to initiatethe NUC process.

In yet another example, a NUC process may be initiated by host device102 if a minimum time has elapsed since a previously performed NUCprocess. In a further example, a NUC process may be initiated by hostdevice 102 if infrared imaging module 100 has experienced a minimumtemperature change since a previously performed NUC process. In a stillfurther example, a NUC process may be continuously initiated andrepeated.

In block 515, after a NUC process initiating event is detected, it isdetermined whether the NUC process should actually be performed. In thisregard, the NUC process may be selectively initiated based on whetherone or more additional conditions are met. For example, in oneembodiment, the NUC process may not be performed unless a minimum timehas elapsed since a previously performed NUC process. In anotherembodiment, the NUC process may not be performed unless infrared imagingmodule 100 has experienced a minimum temperature change since apreviously performed NUC process. Other criteria or conditions may beused in other embodiments. If appropriate criteria or conditions havebeen met, then the flow diagram continues to block 520. Otherwise, theflow diagram returns to block 505.

In the NUC process, blurred image frames may be used to determine NUCterms which may be applied to captured image frames to correct for FPN.As discussed, in one embodiment, the blurred image frames may beobtained by accumulating multiple image frames of a moving scene (e.g.,captured while the scene and/or the thermal imager is in motion). Inanother embodiment, the blurred image frames may be obtained bydefocusing an optical element or other component of the thermal imager.

Accordingly, in block 520 a choice of either approach is provided. Ifthe motion-based approach is used, then the flow diagram continues toblock 525. If the defocus-based approach is used, then the flow diagramcontinues to block 530.

Referring now to the motion-based approach, in block 525 motion isdetected. For example, in one embodiment, motion may be detected basedon the image frames captured by infrared sensors 132. In this regard, anappropriate motion detection process (e.g., an image registrationprocess, a frame-to-frame difference calculation, or other appropriateprocess) may be applied to captured image frames to determine whethermotion is present (e.g., whether static or moving image frames have beencaptured). For example, in one embodiment, it can be determined whetherpixels or regions around the pixels of consecutive image frames havechanged more than a user defined amount (e.g., a percentage and/orthreshold value). If at least a given percentage of pixels have changedby at least the user defined amount, then motion will be detected withsufficient certainty to proceed to block 535.

In another embodiment, motion may be determined on a per pixel basis,wherein only pixels that exhibit significant changes are accumulated toprovide the blurred image frame. For example, counters may be providedfor each pixel and used to ensure that the same number of pixel valuesare accumulated for each pixel, or used to average the pixel valuesbased on the number of pixel values actually accumulated for each pixel.Other types of image-based motion detection may be performed such asperforming a Radon transform.

In another embodiment, motion may be detected based on data provided bymotion sensors 194. In one embodiment, such motion detection may includedetecting whether host device 102 is moving along a relatively straighttrajectory through space. For example, if host device 102 is movingalong a relatively straight trajectory, then it is possible that certainobjects appearing in the imaged scene may not be sufficiently blurred(e.g., objects in the scene that may be aligned with or movingsubstantially parallel to the straight trajectory). Thus, in such anembodiment, the motion detected by motion sensors 194 may be conditionedon host device 102 exhibiting, or not exhibiting, particulartrajectories.

In yet another embodiment, both a motion detection process and motionsensors 194 may be used. Thus, using any of these various embodiments, adetermination can be made as to whether or not each image frame wascaptured while at least a portion of the scene and host device 102 werein motion relative to each other (e.g., which may be caused by hostdevice 102 moving relative to the scene, at least a portion of the scenemoving relative to host device 102, or both).

It is expected that the image frames for which motion was detected mayexhibit some secondary blurring of the captured scene (e.g., blurredthermal image data associated with the scene) due to the thermal timeconstants of infrared sensors 132 (e.g., microbolometer thermal timeconstants) interacting with the scene movement.

In block 535, image frames for which motion was detected areaccumulated. For example, if motion is detected for a continuous seriesof image frames, then the image frames of the series may be accumulated.As another example, if motion is detected for only some image frames,then the non-moving image frames may be skipped and not included in theaccumulation. Thus, a continuous or discontinuous set of image framesmay be selected to be accumulated based on the detected motion.

In block 540, the accumulated image frames are averaged to provide ablurred image frame. Because the accumulated image frames were capturedduring motion, it is expected that actual scene information will varybetween the image frames and thus cause the scene information to befurther blurred in the resulting blurred image frame (block 545).

In contrast, FPN (e.g., caused by one or more components of infraredimaging module 100) will remain fixed over at least short periods oftime and over at least limited changes in scene irradiance duringmotion. As a result, image frames captured in close proximity in timeand space during motion will suffer from identical or at least verysimilar FPN. Thus, although scene information may change in consecutiveimage frames, the FPN will stay essentially constant. By averaging,multiple image frames captured during motion will blur the sceneinformation, but will not blur the FPN. As a result, FPN will remainmore clearly defined in the blurred image frame provided in block 545than the scene information.

In one embodiment, 32 or more image frames are accumulated and averagedin blocks 535 and 540. However, any desired number of image frames maybe used in other embodiments, but with generally decreasing correctionaccuracy as frame count is decreased.

Referring now to the defocus-based approach, in block 530, a defocusoperation may be performed to intentionally defocus the image framescaptured by infrared sensors 132. For example, in one embodiment, one ormore actuators 199 may be used to adjust, move, or otherwise translateoptical element 180, infrared sensor assembly 128, and/or othercomponents of infrared imaging module 100 to cause infrared sensors 132to capture a blurred (e.g., unfocused) image frame of the scene. Othernon-actuator based techniques are also contemplated for intentionallydefocusing infrared image frames such as, for example, manual (e.g.,user-initiated) defocusing.

Although the scene may appear blurred in the image frame, FPN (e.g.,caused by one or more components of infrared imaging module 100) willremain unaffected by the defocusing operation. As a result, a blurredimage frame of the scene will be provided (block 545) with FPN remainingmore clearly defined in the blurred image than the scene information.

In the above discussion, the defocus-based approach has been describedwith regard to a single captured image frame. In another embodiment, thedefocus-based approach may include accumulating multiple image frameswhile the infrared imaging module 100 has been defocused and averagingthe defocused image frames to remove the effects of temporal noise andprovide a blurred image frame in block 545.

Thus, it will be appreciated that a blurred image frame may be providedin block 545 by either the motion-based approach or the defocus-basedapproach. Because much of the scene information will be blurred byeither motion, defocusing, or both, the blurred image frame may beeffectively considered a low pass filtered version of the originalcaptured image frames with respect to scene information.

In block 550, the blurred image frame is processed to determine updatedrow and column FPN terms (e.g., if row and column FPN terms have notbeen previously determined then the updated row and column FPN terms maybe new row and column FPN terms in the first iteration of block 550). Asused in this disclosure, the terms row and column may be usedinterchangeably depending on the orientation of infrared sensors 132and/or other components of infrared imaging module 100.

In one embodiment, block 550 includes determining a spatial FPNcorrection term for each row of the blurred image frame (e.g., each rowmay have its own spatial FPN correction term), and also determining aspatial FPN correction term for each column of the blurred image frame(e.g., each column may have its own spatial FPN correction term). Suchprocessing may be used to reduce the spatial and slowly varying (1/f)row and column FPN inherent in thermal imagers caused by, for example,1/f noise characteristics of amplifiers in ROIC 402 which may manifestas vertical and horizontal stripes in image frames.

Advantageously, by determining spatial row and column FPN terms usingthe blurred image frame, there will be a reduced risk of vertical andhorizontal objects in the actual imaged scene from being mistaken forrow and column noise (e.g., real scene content will be blurred while FPNremains unblurred).

In one embodiment, row and column FPN terms may be determined byconsidering differences between neighboring pixels of the blurred imageframe. For example, FIG. 6 illustrates differences between neighboringpixels in accordance with an embodiment of the disclosure. Specifically,in FIG. 6 a pixel 610 is compared to its 8 nearest horizontal neighbors:d0-d3 on one side and d4-d7 on the other side. Differences between theneighbor pixels can be averaged to obtain an estimate of the offseterror of the illustrated group of pixels. An offset error may becalculated for each pixel in a row or column and the average result maybe used to correct the entire row or column.

To prevent real scene data from being interpreted as noise, upper andlower threshold values may be used (thPix and −thPix). Pixel valuesfalling outside these threshold values (pixels d1 and d4 in thisexample) are not used to obtain the offset error. In addition, themaximum amount of row and column FPN correction may be limited by thesethreshold values.

Further techniques for performing spatial row and column FPN correctionprocessing are set forth in U.S. patent application Ser. No. 12/396,340filed Mar. 2, 2009 which is incorporated herein by reference in itsentirety.

Referring again to FIG. 5, the updated row and column FPN termsdetermined in block 550 are stored (block 552) and applied (block 555)to the blurred image frame provided in block 545. After these terms areapplied, some of the spatial row and column FPN in the blurred imageframe may be reduced. However, because such terms are applied generallyto rows and columns, additional FPN may remain such as spatiallyuncorrelated FPN associated with pixel to pixel drift or other causes.Neighborhoods of spatially correlated FPN may also remain which may notbe directly associated with individual rows and columns. Accordingly,further processing may be performed as discussed below to determine NUCterms.

In block 560, local contrast values (e.g., edges or absolute values ofgradients between adjacent or small groups of pixels) in the blurredimage frame are determined. If scene information in the blurred imageframe includes contrasting areas that have not been significantlyblurred (e.g., high contrast edges in the original scene data), thensuch features may be identified by a contrast determination process inblock 560.

For example, local contrast values in the blurred image frame may becalculated, or any other desired type of edge detection process may beapplied to identify certain pixels in the blurred image as being part ofan area of local contrast. Pixels that are marked in this manner may beconsidered as containing excessive high spatial frequency sceneinformation that would be interpreted as FPN (e.g., such regions maycorrespond to portions of the scene that have not been sufficientlyblurred). As such, these pixels may be excluded from being used in thefurther determination of NUC terms. In one embodiment, such contrastdetection processing may rely on a threshold that is higher than theexpected contrast value associated with FPN (e.g., pixels exhibiting acontrast value higher than the threshold may be considered to be sceneinformation, and those lower than the threshold may be considered to beexhibiting FPN).

In one embodiment, the contrast determination of block 560 may beperformed on the blurred image frame after row and column FPN terms havebeen applied to the blurred image frame (e.g., as shown in FIG. 5). Inanother embodiment, block 560 may be performed prior to block 550 todetermine contrast before row and column FPN terms are determined (e.g.,to prevent scene based contrast from contributing to the determinationof such terms).

Following block 560, it is expected that any high spatial frequencycontent remaining in the blurred image frame may be generally attributedto spatially uncorrelated FPN. In this regard, following block 560, muchof the other noise or actual desired scene based information has beenremoved or excluded from the blurred image frame due to: intentionalblurring of the image frame (e.g., by motion or defocusing in blocks 520through 545), application of row and column FPN terms (block 555), andcontrast determination (block 560).

Thus, it can be expected that following block 560, any remaining highspatial frequency content (e.g., exhibited as areas of contrast ordifferences in the blurred image frame) may be attributed to spatiallyuncorrelated FPN. Accordingly, in block 565, the blurred image frame ishigh pass filtered. In one embodiment, this may include applying a highpass filter to extract the high spatial frequency content from theblurred image frame. In another embodiment, this may include applying alow pass filter to the blurred image frame and taking a differencebetween the low pass filtered image frame and the unfiltered blurredimage frame to obtain the high spatial frequency content. In accordancewith various embodiments of the present disclosure, a high pass filtermay be implemented by calculating a mean difference between a sensorsignal (e.g., a pixel value) and its neighbors.

In block 570, a flat field correction process is performed on the highpass filtered blurred image frame to determine updated NUC terms (e.g.,if a NUC process has not previously been performed then the updated NUCterms may be new NUC terms in the first iteration of block 570).

For example, FIG. 7 illustrates a flat field correction technique 700 inaccordance with an embodiment of the disclosure. In FIG. 7, a NUC termmay be determined for each pixel 710 of the blurred image frame usingthe values of its neighboring pixels 712 to 726. For each pixel 710,several gradients may be determined based on the absolute differencebetween the values of various adjacent pixels. For example, absolutevalue differences may be determined between: pixels 712 and 714 (a leftto right diagonal gradient), pixels 716 and 718 (a top to bottomvertical gradient), pixels 720 and 722 (a right to left diagonalgradient), and pixels 724 and 726 (a left to right horizontal gradient).

These absolute differences may be summed to provide a summed gradientfor pixel 710. A weight value may be determined for pixel 710 that isinversely proportional to the summed gradient. This process may beperformed for all pixels 710 of the blurred image frame until a weightvalue is provided for each pixel 710. For areas with low gradients(e.g., areas that are blurry or have low contrast), the weight valuewill be close to one. Conversely, for areas with high gradients, theweight value will be zero or close to zero. The update to the NUC termas estimated by the high pass filter is multiplied with the weightvalue.

In one embodiment, the risk of introducing scene information into theNUC terms can be further reduced by applying some amount of temporaldamping to the NUC term determination process. For example, a temporaldamping factor λ between 0 and 1 may be chosen such that the new NUCterm (NUC_(NEW)) stored is a weighted average of the old NUC term(NUC_(OLD)) and the estimated updated NUC term (NUC_(UPDATE)). In oneembodiment, this can be expressed asNUC_(NEW)=λ·NUC_(OLD)+(1−λ)·(NUC_(OLD)+NUC_(UPDATE)).

Although the determination of NUC terms has been described with regardto gradients, local contrast values may be used instead whereappropriate. Other techniques may also be used such as, for example,standard deviation calculations. Other types flat field correctionprocesses may be performed to determine NUC terms including, forexample, various processes identified in U.S. Pat. No. 6,028,309 issuedFeb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov. 2, 2004, and U.S.patent application Ser. No. 12/114,865 filed May 5, 2008, which areincorporated herein by reference in their entirety.

Referring again to FIG. 5, block 570 may include additional processingof the NUC terms. For example, in one embodiment, to preserve the scenesignal mean, the sum of all NUC terms may be normalized to zero bysubtracting the NUC term mean from each NUC term. Also in block 570, toavoid row and column noise from affecting the NUC terms, the mean valueof each row and column may be subtracted from the NUC terms for each rowand column. As a result, row and column FPN filters using the row andcolumn FPN terms determined in block 550 may be better able to filterout row and column noise in further iterations (e.g., as further shownin FIG. 8) after the NUC terms are applied to captured images (e.g., inblock 580 further discussed herein). In this regard, the row and columnFPN filters may in general use more data to calculate the per row andper column offset coefficients (e.g., row and column FPN terms) and maythus provide a more robust alternative for reducing spatially correlatedFPN than the NUC terms which are based on high pass filtering to capturespatially uncorrelated noise.

In blocks 571-573, additional high pass filtering and furtherdeterminations of updated NUC terms may be optionally performed toremove spatially correlated FPN with lower spatial frequency thanpreviously removed by row and column FPN terms. In this regard, somevariability in infrared sensors 132 or other components of infraredimaging module 100 may result in spatially correlated FPN noise thatcannot be easily modeled as row or column noise. Such spatiallycorrelated FPN may include, for example, window defects on a sensorpackage or a cluster of infrared sensors 132 that respond differently toirradiance than neighboring infrared sensors 132. In one embodiment,such spatially correlated FPN may be mitigated with an offsetcorrection. If the amount of such spatially correlated FPN issignificant, then the noise may also be detectable in the blurred imageframe. Since this type of noise may affect a neighborhood of pixels, ahigh pass filter with a small kernel may not detect the FPN in theneighborhood (e.g., all values used in high pass filter may be takenfrom the neighborhood of affected pixels and thus may be affected by thesame offset error). For example, if the high pass filtering of block 565is performed with a small kernel (e.g., considering only immediatelyadjacent pixels that fall within a neighborhood of pixels affected byspatially correlated FPN), then broadly distributed spatially correlatedFPN may not be detected.

For example, FIG. 11 illustrates spatially correlated FPN in aneighborhood of pixels in accordance with an embodiment of thedisclosure. As shown in a sample image frame 1100, a neighborhood ofpixels 1110 may exhibit spatially correlated FPN that is not preciselycorrelated to individual rows and columns and is distributed over aneighborhood of several pixels (e.g., a neighborhood of approximately 4by 4 pixels in this example). Sample image frame 1100 also includes aset of pixels 1120 exhibiting substantially uniform response that arenot used in filtering calculations, and a set of pixels 1130 that areused to estimate a low pass value for the neighborhood of pixels 1110.In one embodiment, pixels 1130 may be a number of pixels divisible bytwo in order to facilitate efficient hardware or software calculations.

Referring again to FIG. 5, in blocks 571-573, additional high passfiltering and further determinations of updated NUC terms may beoptionally performed to remove spatially correlated FPN such asexhibited by pixels 1110. In block 571, the updated NUC terms determinedin block 570 are applied to the blurred image frame. Thus, at this time,the blurred image frame will have been initially corrected for spatiallycorrelated FPN (e.g., by application of the updated row and column FPNterms in block 555), and also initially corrected for spatiallyuncorrelated FPN (e.g., by application of the updated NUC terms appliedin block 571).

In block 572, a further high pass filter is applied with a larger kernelthan was used in block 565, and further updated NUC terms may bedetermined in block 573. For example, to detect the spatially correlatedFPN present in pixels 1110, the high pass filter applied in block 572may include data from a sufficiently large enough neighborhood of pixelssuch that differences can be determined between unaffected pixels (e.g.,pixels 1120) and affected pixels (e.g., pixels 1110). For example, a lowpass filter with a large kernel can be used (e.g., an N by N kernel thatis much greater than 3 by 3 pixels) and the results may be subtracted toperform appropriate high pass filtering.

In one embodiment, for computational efficiency, a sparse kernel may beused such that only a small number of neighboring pixels inside an N byN neighborhood are used. For any given high pass filter operation usingdistant neighbors (e.g., a large kernel), there is a risk of modelingactual (potentially blurred) scene information as spatially correlatedFPN. Accordingly, in one embodiment, the temporal damping factor λ maybe set close to 1 for updated NUC terms determined in block 573.

In various embodiments, blocks 571-573 may be repeated (e.g., cascaded)to iteratively perform high pass filtering with increasing kernel sizesto provide further updated NUC terms further correct for spatiallycorrelated FPN of desired neighborhood sizes. In one embodiment, thedecision to perform such iterations may be determined by whetherspatially correlated FPN has actually been removed by the updated NUCterms of the previous performance of blocks 571-573.

After blocks 571-573 are finished, a decision is made regarding whetherto apply the updated NUC terms to captured image frames (block 574). Forexample, if an average of the absolute value of the NUC terms for theentire image frame is less than a minimum threshold value, or greaterthan a maximum threshold value, the NUC terms may be deemed spurious orunlikely to provide meaningful correction. Alternatively, thresholdingcriteria may be applied to individual pixels to determine which pixelsreceive updated NUC terms. In one embodiment, the threshold values maycorrespond to differences between the newly calculated NUC terms andpreviously calculated NUC terms. In another embodiment, the thresholdvalues may be independent of previously calculated NUC terms. Othertests may be applied (e.g., spatial correlation tests) to determinewhether the NUC terms should be applied.

If the NUC terms are deemed spurious or unlikely to provide meaningfulcorrection, then the flow diagram returns to block 505. Otherwise, thenewly determined NUC terms are stored (block 575) to replace previousNUC terms (e.g., determined by a previously performed iteration of FIG.5) and applied (block 580) to captured image frames.

FIG. 8 illustrates various image processing techniques of FIG. 5 andother operations applied in an image processing pipeline 800 inaccordance with an embodiment of the disclosure. In this regard,pipeline 800 identifies various operations of FIG. 5 in the context ofan overall iterative image processing scheme for correcting image framesprovided by infrared imaging module 100. In some embodiments, pipeline800 may be provided by processing module 160 or processor 195 (both alsogenerally referred to as a processor) operating on image frames capturedby infrared sensors 132.

Image frames captured by infrared sensors 132 may be provided to a frameaverager 804 that integrates multiple image frames to provide imageframes 802 with an improved signal to noise ratio. Frame averager 804may be effectively provided by infrared sensors 132, ROIC 402, and othercomponents of infrared sensor assembly 128 that are implemented tosupport high image capture rates. For example, in one embodiment,infrared sensor assembly 128 may capture infrared image frames at aframe rate of 240 Hz (e.g., 240 images per second). In this embodiment,such a high frame rate may be implemented, for example, by operatinginfrared sensor assembly 128 at relatively low voltages (e.g.,compatible with mobile telephone voltages) and by using a relativelysmall array of infrared sensors 132 (e.g., an array of 64 by 64 infraredsensors in one embodiment).

In one embodiment, such infrared image frames may be provided frominfrared sensor assembly 128 to processing module 160 at a high framerate (e.g., 240 Hz or other frame rates). In another embodiment,infrared sensor assembly 128 may integrate over longer time periods, ormultiple time periods, to provide integrated (e.g., averaged) infraredimage frames to processing module 160 at a lower frame rate (e.g., 30Hz, 9 Hz, or other frame rates). Further information regardingimplementations that may be used to provide high image capture rates maybe found in U.S. Provisional Patent Application No. 61/495,879 filedJun. 10, 2011 which is incorporated herein by reference in its entirety.

Image frames 802 proceed through pipeline 800 where they are adjusted byvarious terms, temporally filtered, used to determine the variousadjustment terms, and gain compensated.

In blocks 810 and 814, factory gain terms 812 and factory offset terms816 are applied to image frames 802 to compensate for gain and offsetdifferences, respectively, between the various infrared sensors 132and/or other components of infrared imaging module 100 determined duringmanufacturing and testing.

In block 580, NUC terms 817 are applied to image frames 802 to correctfor FPN as discussed. In one embodiment, if NUC terms 817 have not yetbeen determined (e.g., before a NUC process has been initiated), thenblock 580 may not be performed or initialization values may be used forNUC terms 817 that result in no alteration to the image data (e.g.,offsets for every pixel would be equal to zero).

In blocks 818 and 822, column FPN terms 820 and row FPN terms 824,respectively, are applied to image frames 802. Column FPN terms 820 androw FPN terms 824 may be determined in accordance with block 550 asdiscussed. In one embodiment, if the column FPN terms 820 and row FPNterms 824 have not yet been determined (e.g., before a NUC process hasbeen initiated), then blocks 818 and 822 may not be performed orinitialization values may be used for the column FPN terms 820 and rowFPN terms 824 that result in no alteration to the image data (e.g.,offsets for every pixel would be equal to zero).

In block 826, temporal filtering is performed on image frames 802 inaccordance with a temporal noise reduction (TNR) process. FIG. 9illustrates a TNR process in accordance with an embodiment of thedisclosure. In FIG. 9, a presently received image frame 802 a and apreviously temporally filtered image frame 802 b are processed todetermine a new temporally filtered image frame 802 e. Image frames 802a and 802 b include local neighborhoods of pixels 803 a and 803 bcentered around pixels 805 a and 805 b, respectively. Neighborhoods 803a and 803 b correspond to the same locations within image frames 802 aand 802 b and are subsets of the total pixels in image frames 802 a and802 b. In the illustrated embodiment, neighborhoods 803 a and 803 binclude areas of 5 by 5 pixels. Other neighborhood sizes may be used inother embodiments.

Differences between corresponding pixels of neighborhoods 803 a and 803b are determined and averaged to provide an averaged delta value 805 cfor the location corresponding to pixels 805 a and 805 b. Averaged deltavalue 805 c may be used to determine weight values in block 807 to beapplied to pixels 805 a and 805 b of image frames 802 a and 802 b.

In one embodiment, as shown in graph 809, the weight values determinedin block 807 may be inversely proportional to averaged delta value 805 csuch that weight values drop rapidly towards zero when there are largedifferences between neighborhoods 803 a and 803 b. In this regard, largedifferences between neighborhoods 803 a and 803 b may indicate thatchanges have occurred within the scene (e.g., due to motion) and pixels802 a and 802 b may be appropriately weighted, in one embodiment, toavoid introducing blur across frame-to-frame scene changes. Otherassociations between weight values and averaged delta value 805 c may beused in various embodiments.

The weight values determined in block 807 may be applied to pixels 802 aand 802 b to determine a value for corresponding pixel 805 e of imageframe 802 e (block 811). In this regard, pixel 805 e may have a valuethat is a weighted average (or other combination) of pixels 805 a and805 b, depending on averaged delta value 805 c and the weight valuesdetermined in block 807.

For example, pixel 805 e of temporally filtered image frame 802 e may bea weighted sum of pixels 805 a and 805 b of image frames 802 a and 802b. If the average difference between pixels 805 a and 805 b is due tonoise, then it may be expected that the average change betweenneighborhoods 805 a and 805 b will be close to zero (e.g., correspondingto the average of uncorrelated changes). Under such circumstances, itmay be expected that the sum of the differences between neighborhoods805 a and 805 b will be close to zero. In this case, pixel 805 a ofimage frame 802 a may both be appropriately weighted so as to contributeto the value of pixel 805 e.

However, if the sum of such differences is not zero (e.g., evendiffering from zero by a small amount in one embodiment), then thechanges may be interpreted as being attributed to motion instead ofnoise. Thus, motion may be detected based on the average changeexhibited by neighborhoods 805 a and 805 b. Under these circumstances,pixel 805 a of image frame 802 a may be weighted heavily, while pixel805 b of image frame 802 b may be weighted lightly.

Other embodiments are also contemplated. For example, although averageddelta value 805 c has been described as being determined based onneighborhoods 805 a and 805 b, in other embodiments averaged delta value805 c may be determined based on any desired criteria (e.g., based onindividual pixels or other types of groups of sets of pixels).

In the above embodiments, image frame 802 a has been described as apresently received image frame and image frame 802 b has been describedas a previously temporally filtered image frame. In another embodiment,image frames 802 a and 802 b may be first and second image framescaptured by infrared imaging module 100 that have not been temporallyfiltered.

FIG. 10 illustrates further implementation details in relation to theTNR process of block 826. As shown in FIG. 10, image frames 802 a and802 b may be read into line buffers 1010 a and 1010 b, respectively, andimage frame 802 b (e.g., the previous image frame) may be stored in aframe buffer 1020 before being read into line buffer 1010 b. In oneembodiment, line buffers 1010 a-b and frame buffer 1020 may beimplemented by a block of random access memory (RAM) provided by anyappropriate component of infrared imaging module 100 and/or host device102.

Referring again to FIG. 8, image frame 802 e may be passed to anautomatic gain compensation block 828 for further processing to providea result image frame 830 that may be used by host device 102 as desired.

FIG. 8 further illustrates various operations that may be performed todetermine row and column FPN terms and NUC terms as discussed. In oneembodiment, these operations may use image frames 802 e as shown in FIG.8. Because image frames 802 e have already been temporally filtered, atleast some temporal noise may be removed and thus will not inadvertentlyaffect the determination of row and column FPN terms 824 and 820 and NUCterms 817. In another embodiment, non-temporally filtered image frames802 may be used.

In FIG. 8, blocks 510, 515, and 520 of FIG. 5 are collectivelyrepresented together. As discussed, a NUC process may be selectivelyinitiated and performed in response to various NUC process initiatingevents and based on various criteria or conditions. As also discussed,the NUC process may be performed in accordance with a motion-basedapproach (blocks 525, 535, and 540) or a defocus-based approach (block530) to provide a blurred image frame (block 545). FIG. 8 furtherillustrates various additional blocks 550, 552, 555, 560, 565, 570, 571,572, 573, and 575 previously discussed with regard to FIG. 5.

As shown in FIG. 8, row and column FPN terms 824 and 820 and NUC terms817 may be determined and applied in an iterative fashion such thatupdated teems are determined using image frames 802 to which previousterms have already been applied. As a result, the overall process ofFIG. 8 may repeatedly update and apply such terms to continuously reducethe noise in image frames 830 to be used by host device 102.

Referring again to FIG. 10, further implementation details areillustrated for various blocks of FIGS. 5 and 8 in relation to pipeline800. For example, blocks 525, 535, and 540 are shown as operating at thenormal frame rate of image frames 802 received by pipeline 800. In theembodiment shown in FIG. 10, the determination made in block 525 isrepresented as a decision diamond used to determine whether a givenimage frame 802 has sufficiently changed such that it may be consideredan image frame that will enhance the blur if added to other image framesand is therefore accumulated (block 535 is represented by an arrow inthis embodiment) and averaged (block 540).

Also in FIG. 10, the determination of column FPN terms 820 (block 550)is shown as operating at an update rate that in this example is 1/32 ofthe sensor frame rate (e.g., normal frame rate) due to the averagingperformed in block 540. Other update rates may be used in otherembodiments. Although only column FPN terms 820 are identified in FIG.10, row FPN terms 824 may be implemented in a similar fashion at thereduced frame rate.

FIG. 10 also illustrates further implementation details in relation tothe NUC determination process of block 570. In this regard, the blurredimage frame may be read to a line buffer 1030 (e.g., implemented by ablock of RAM provided by any appropriate component of infrared imagingmodule 100 and/or host device 102). The flat field correction technique700 of FIG. 7 may be performed on the blurred image frame.

In view of the present disclosure, it will be appreciated thattechniques described herein may be used to remove various types of FPN(e.g., including very high amplitude FPN) such as spatially correlatedrow and column FPN and spatially uncorrelated FPN.

Other embodiments are also contemplated. For example, in one embodiment,the rate at which row and column FPN terms and/or NUC terms are updatedcan be inversely proportional to the estimated amount of blur in theblurred image frame and/or inversely proportional to the magnitude oflocal contrast values (e.g., determined in block 560).

In various embodiments, the described techniques may provide advantagesover conventional shutter-based noise correction techniques. Forexample, by using a shutterless process, a shutter (e.g., such asshutter 105) need not be provided, thus permitting reductions in size,weight, cost, and mechanical complexity. Power and maximum voltagesupplied to, or generated by, infrared imaging module 100 may also bereduced if a shutter does not need to be mechanically operated.Reliability will be improved by removing the shutter as a potentialpoint of failure. A shutterless process also eliminates potential imageinterruption caused by the temporary blockage of the imaged scene by ashutter.

Also, by correcting for noise using intentionally blurred image framescaptured from a real world scene (not a uniform scene provided by ashutter), noise correction may be performed on image frames that haveirradiance levels similar to those of the actual scene desired to beimaged. This can improve the accuracy and effectiveness of noisecorrection terms determined in accordance with the various describedtechniques.

As discussed, in various embodiments, infrared imaging module 100 may beconfigured to operate at low voltage levels. In particular, infraredimaging module 100 may be implemented with circuitry configured tooperate at low power and/or in accordance with other parameters thatpermit infrared imaging module 100 to be conveniently and effectivelyimplemented in various types of host devices 102, such as mobile devicesand other devices.

For example, FIG. 12 illustrates a block diagram of anotherimplementation of infrared sensor assembly 128 including infraredsensors 132 and an LDO 1220 in accordance with an embodiment of thedisclosure. As shown, FIG. 12 also illustrates various components 1202,1204, 1205, 1206, 1208, and 1210 which may implemented in the same orsimilar manner as corresponding components previously described withregard to FIG. 4. FIG. 12 also illustrates bias correction circuitry1212 which may be used to adjust one or more bias voltages provided toinfrared sensors 132 (e.g., to compensate for temperature changes,self-heating, and/or other factors).

In some embodiments, LDO 1220 may be provided as part of infrared sensorassembly 128 (e.g., on the same chip and/or wafer level package as theROIC). For example, LDO 1220 may be provided as part of an FPA withinfrared sensor assembly 128. As discussed, such implementations mayreduce power supply noise introduced to infrared sensor assembly 128 andthus provide an improved PSRR. In addition, by implementing the LDO withthe ROIC, less die area may be consumed and fewer discrete die (orchips) are needed.

LDO 1220 receives an input voltage provided by a power source 1230 overa supply line 1232. LDO 1220 provides an output voltage to variouscomponents of infrared sensor assembly 128 over supply lines 1222. Inthis regard, LDO 1220 may provide substantially identical regulatedoutput voltages to various components of infrared sensor assembly 128 inresponse to a single input voltage received from power source 1230, inaccordance with various techniques described in, for example, U.S.patent application Ser. No. 14/101,245 filed Dec. 9, 2013 incorporatedherein by reference in its entirety.

For example, in some embodiments, power source 1230 may provide an inputvoltage in a range of approximately 2.8 volts to approximately 11 volts(e.g., approximately 2.8 volts in one embodiment), and LDO 1220 mayprovide an output voltage in a range of approximately 1.5 volts toapproximately 2.8 volts (e.g., approximately 2.8, 2.5, 2.4, and/or lowervoltages in various embodiments). In this regard, LDO 1220 may be usedto provide a consistent regulated output voltage, regardless of whetherpower source 1230 is implemented with a conventional voltage range ofapproximately 9 volts to approximately 11 volts, or a low voltage suchas approximately 2.8 volts. As such, although various voltage ranges areprovided for the input and output voltages, it is contemplated that theoutput voltage of LDO 1220 will remain fixed despite changes in theinput voltage.

The implementation of LDO 1220 as part of infrared sensor assembly 128provides various advantages over conventional power implementations forFPAs. For example, conventional FPAs typically rely on multiple powersources, each of which may be provided separately to the FPA, andseparately distributed to the various components of the FPA. Byregulating a single power source 1230 by LDO 1220, appropriate voltagesmay be separately provided (e.g., to reduce possible noise) to allcomponents of infrared sensor assembly 128 with reduced complexity. Theuse of LDO 1220 also allows infrared sensor assembly 128 to operate in aconsistent manner, even if the input voltage from power source 1230changes (e.g., if the input voltage increases or decreases as a resultof charging or discharging a battery or other type of device used forpower source 1230).

The various components of infrared sensor assembly 128 shown in FIG. 12may also be implemented to operate at lower voltages than conventionaldevices. For example, as discussed, LDO 1220 may be implemented toprovide a low voltage (e.g., approximately 2.5 volts). This contrastswith the multiple higher voltages typically used to power conventionalFPAs, such as: approximately 3.3 volts to approximately 5 volts used topower digital circuitry; approximately 3.3 volts used to power analogcircuitry; and approximately 9 volts to approximately 11 volts used topower loads. Also, in some embodiments, the use of LDO 1220 may reduceor eliminate the need for a separate negative reference voltage to beprovided to infrared sensor assembly 128.

Additional aspects of the low voltage operation of infrared sensorassembly 128 may be further understood with reference to FIG. 13. FIG.13 illustrates a circuit diagram of a portion of infrared sensorassembly 128 of FIG. 12 in accordance with an embodiment of thedisclosure. In particular, FIG. 13 illustrates additional components ofbias correction circuitry 1212 (e.g., components 1326, 1330, 1332, 1334,1336, 1338, and 1341) connected to LDO 1220 and infrared sensors 132.For example, bias correction circuitry 1212 may be used to compensatefor temperature-dependent changes in bias voltages in accordance with anembodiment of the present disclosure. The operation of such additionalcomponents may be further understood with reference to similarcomponents identified in U.S. Pat. No. 7,679,048 issued Mar. 16, 2010which is hereby incorporated by reference in its entirety. Infraredsensor assembly 128 may also be implemented in accordance with thevarious components identified in U.S. Pat. No. 6,812,465 issued Nov. 2,2004 which is hereby incorporated by reference in its entirety.

In various embodiments, some or all of the bias correction circuitry1212 may be implemented on a global array basis as shown in FIG. 13(e.g., used for all infrared sensors 132 collectively in an array). Inother embodiments, some or all of the bias correction circuitry 1212 maybe implemented an individual sensor basis (e.g., entirely or partiallyduplicated for each infrared sensor 132). In some embodiments, biascorrection circuitry 1212 and other components of FIG. 13 may beimplemented as part of ROIC 1202.

As shown in FIG. 13, LDO 1220 provides a load voltage Vload to biascorrection circuitry 1212 along one of supply lines 1222. As discussed,in some embodiments, Vload may be approximately 2.5 volts whichcontrasts with larger voltages of approximately 9 volts to approximately11 volts that may be used as load voltages in conventional infraredimaging devices.

Based on Vload, bias correction circuitry 1212 provides a sensor biasvoltage Vbolo at a node 1360. Vbolo may be distributed to one or moreinfrared sensors 132 through appropriate switching circuitry 1370 (e.g.,represented by broken lines in FIG. 13). In some examples, switchingcircuitry 1370 may be implemented in accordance with appropriatecomponents identified in U.S. Pat. Nos. 6,812,465 and 7,679,048previously referenced herein.

Each infrared sensor 132 includes a node 1350 which receives Vbolothrough switching circuitry 1370, and another node 1352 which may beconnected to ground, a substrate, and/or a negative reference voltage.In some embodiments, the voltage at node 1360 may be substantially thesame as Vbolo provided at nodes 1350. In other embodiments, the voltageat node 1360 may be adjusted to compensate for possible voltage dropsassociated with switching circuitry 1370 and/or other factors.

Vbolo may be implemented with lower voltages than are typically used forconventional infrared sensor biasing. In one embodiment, Vbolo may be ina range of approximately 0.2 volts to approximately 0.7 volts. Inanother embodiment, Vbolo may be in a range of approximately 0.4 voltsto approximately 0.6 volts. In another embodiment, Vbolo may beapproximately 0.5 volts. In contrast, conventional infrared sensorstypically use bias voltages of approximately 1 volt.

The use of a lower bias voltage for infrared sensors 132 in accordancewith the present disclosure permits infrared sensor assembly 128 toexhibit significantly reduced power consumption in comparison withconventional infrared imaging devices. In particular, the powerconsumption of each infrared sensor 132 is reduced by the square of thebias voltage. As a result, a reduction from, for example, 1.0 volt to0.5 volts provides a significant reduction in power, especially whenapplied to many infrared sensors 132 in an infrared sensor array. Thisreduction in power may also result in reduced self-heating of infraredsensor assembly 128.

In accordance with additional embodiments of the present disclosure,various techniques are provided for reducing the effects of noise inimage frames provided by infrared imaging devices operating at lowvoltages. In this regard, when infrared sensor assembly 128 is operatedwith low voltages as described, noise, self-heating, and/or otherphenomena may, if uncorrected, become more pronounced in image framesprovided by infrared sensor assembly 128.

For example, referring to FIG. 13, when LDO 1220 maintains Vload at alow voltage in the manner described herein, Vbolo will also bemaintained at its corresponding low voltage and the relative size of itsoutput signals may be reduced. As a result, noise, self-heating, and/orother phenomena may have a greater effect on the smaller output signalsread out from infrared sensors 132, resulting in variations (e.g.,errors) in the output signals. If uncorrected, these variations may beexhibited as noise in the image frames. Moreover, although low voltageoperation may reduce the overall amount of certain phenomena (e.g.,self-heating), the smaller output signals may permit the remaining errorsources (e.g., residual self-heating) to have a disproportionate effecton the output signals during low voltage operation.

To compensate for such phenomena, infrared sensor assembly 128, infraredimaging module 100, and/or host device 102 may be implemented withvarious array sizes, frame rates, and/or frame averaging techniques. Forexample, as discussed, a variety of different array sizes arecontemplated for infrared sensors 132. In some embodiments, infraredsensors 132 may be implemented with array sizes ranging from 32 by 32 to160 by 120 infrared sensors 132. Other example array sizes include 80 by64, 80 by 60, 64 by 64, and 64 by 32. Any desired array size may beused.

Advantageously, when implemented with such relatively small array sizes,infrared sensor assembly 128 may provide image frames at relatively highframe rates without requiring significant changes to ROIC and relatedcircuitry. For example, in some embodiments, frame rates may range fromapproximately 120 Hz to approximately 480 Hz.

In some embodiments, the array size and the frame rate may be scaledrelative to each other (e.g., in an inversely proportional manner orotherwise) such that larger arrays are implemented with lower framerates, and smaller arrays are implemented with higher frame rates. Forexample, in one embodiment, an array of 160 by 120 may provide a framerate of approximately 120 Hz. In another embodiment, an array of 80 by60 may provide a correspondingly higher frame rate of approximately 240Hz. Other frame rates are also contemplated.

By scaling the array size and the frame rate relative to each other, theparticular readout timing of rows and/or columns of the FPA may remainconsistent, regardless of the actual FPA size or frame rate. In oneembodiment, the readout timing may be approximately 63 microseconds perrow or column.

As previously discussed with regard to FIG. 8, the image frames capturedby infrared sensors 132 may be provided to a frame averager 804 thatintegrates multiple image frames to provide image frames 802 (e.g.,processed image frames) with a lower frame rate (e.g., approximately 30Hz, approximately 60 Hz, or other frame rates) and with an improvedsignal to noise ratio. In particular, by averaging the high frame rateimage frames provided by a relatively small FPA, image noiseattributable to low voltage operation may be effectively averaged outand/or substantially reduced in image frames 802. Accordingly, infraredsensor assembly 128 may be operated at relatively low voltages providedby LDO 1220 as discussed without experiencing additional noise andrelated side effects in the resulting image frames 802 after processingby frame averager 804.

Other embodiments are also contemplated. For example, although a singlearray of infrared sensors 132 is illustrated, it is contemplated thatmultiple such arrays may be used together to provide higher resolutionimage frames (e.g., a scene may be imaged across multiple such arrays).Such arrays may be provided in multiple infrared sensor assemblies 128and/or provided in the same infrared sensor assembly 128. Each sucharray may be operated at low voltages as described, and also may beprovided with associated ROIC circuitry such that each array may stillbe operated at a relatively high frame rate. The high frame rate imageframes provided by such arrays may be averaged by shared or dedicatedframe averagers 804 to reduce and/or eliminate noise associated with lowvoltage operation. As a result, high resolution infrared images may beobtained while still operating at low voltages.

In various embodiments, infrared sensor assembly 128 may be implementedwith appropriate dimensions to permit infrared imaging module 100 to beused with a small form factor socket 104, such as a socket used formobile devices. For example, in some embodiments, infrared sensorassembly 128 may be implemented with a chip size in a range ofapproximately 4.0 mm by approximately 4.0 mm to approximately 5.5 mm byapproximately 5.5 mm (e.g., approximately 4.0 mm by approximately 5.5 mmin one example). Infrared sensor assembly 128 may be implemented withsuch sizes or other appropriate sizes to permit use with socket 104implemented with various sizes such as: 8.5 mm by 8.5 mm, 8.5 mm by 5.9mm, 6.0 mm by 6.0 mm, 5.5 mm by 5.5 mm, 4.5 mm by 4.5 mm, and/or othersocket sizes such as, for example, those identified in Table 1 of U.S.Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011incorporated herein by reference in its entirety.

As further described with regard to FIGS. 14-23E, various imageprocessing techniques are described which may be applied, for example,to infrared images (e.g., thermal images) to reduce noise within theinfrared images (e.g., improve image detail and/or image quality) and/orprovide non-uniformity correction.

Although FIGS. 14-23E will be primarily described with regard to asystem 2100, the described techniques may be performed by processingmodule 160 or processor 195 (both also generally referred to as aprocessor) operating on image frames captured by infrared sensors 132,and vice versa.

In some embodiments, the techniques described with regard to FIGS.14-22B be used to perform operations of block 550 (see FIGS. 5 and 8) todetermine row and/or column FPN terms. For example, such techniques maybe applied to intentionally blurred images provided by block 545 ofFIGS. 5 and 8. In some embodiments, the techniques described with regardto FIGS. 23A-E may be used in place of and/or in addition to theoperations of blocks 565-573 (see FIGS. 5 and 8) to estimate FPN and/ordetermine NUC terms.

Referring now to FIGS. 14-22B, a significant portion of noise may bedefined as row and column noise. This type of noise may be explained bynon-linearities in a Read Out Integrated Circuit (ROIC). This type ofnoise, if not eliminated, may manifest as vertical and horizontalstripes in the final image and human observers are particularlysensitive to these types of image artifacts. Other systems relying onimagery from infrared sensors, such as, for example, automatic targettrackers may also suffer from performance degradation, if row and columnnoise is present.

Because of non-linear behavior of infrared detectors and read-outintegrated circuit (ROIC) assemblies, even when a shutter operation orexternal black body calibration is performed, there may be residual rowand column noise (e.g., the scene being imaged may not have the exactsame temperature as the shutter). The amount of row and column noise mayincrease over time, after offset calibration, increasing asymptoticallyto some maximum value. In one aspect, this may be referred to as 1/ftype noise.

In any given frame, the row and column noise may be viewed as highfrequency spatial noise. Conventionally, this type of noise may bereduced using filters in the spatial domain (e.g., local linear ornon-linear low pass filters) or the frequency domain (e.g., low passfilters in Fourier or Wavelet space). However, these filters may havenegative side effects, such as blurring of the image and potential lossof faint details.

It should be appreciated by those skilled in the art that any referenceto a column or a row may include a partial column or a partial row andthat the terms “row” and “column” are interchangeable and not limiting.Thus, without departing from the scope of the invention, the term “row”may be used to describe a row or a column, and likewise, the term“column” may be used to describe a row or a column, depending upon theapplication.

FIG. 14 shows a block diagram of system 2100 (e.g., an infrared camera)for infrared image capturing and processing in accordance with anembodiment. In some embodiments, system 2100 may be implemented byinfrared imaging module 100, host device 102, infrared sensor assembly128, and/or various components described herein (e.g., see FIGS. 1-13).Accordingly, although various techniques are described with regard tosystem 2100, such techniques may be similarly applied to infraredimaging module 100, host device 102, infrared sensor assembly 128,and/or various components described herein, and vice versa.

The system 2100 comprises, in one implementation, a processing component2110, a memory component 2120, an image capture component 2130, acontrol component 2140, and a display component 2150. Optionally, thesystem 2100 may include a sensing component 2160.

The system 2100 may represent an infrared imaging device, such as aninfrared camera, to capture and process images, such as video images ofa scene 2170. The system 2100 may represent any type of infrared cameraadapted to detect infrared radiation and provide representative data andinformation (e.g., infrared image data of a scene). For example, thesystem 2100 may represent an infrared camera that is directed to thenear, middle, and/or far infrared spectrums. In another example, theinfrared image data may comprise non-uniform data (e.g., real image datathat is not from a shutter or black body) of the scene 2170, forprocessing, as set forth herein. The system 2100 may comprise a portabledevice and may be incorporated, e.g., into a vehicle (e.g., anautomobile or other type of land-based vehicle, an aircraft, or aspacecraft) or a non-mobile installation requiring infrared images to bestored and/or displayed.

In various embodiments, the processing component 2110 comprises aprocessor, such as one or more of a microprocessor, a single-coreprocessor, a multi-core processor, a microcontroller, a logic device(e.g., a programmable logic device (PLD) configured to performprocessing functions), a digital signal processing (DSP) device, etc.The processing component 2110 may be adapted to interface andcommunicate with components 2120, 2130, 2140, and 2150 to perform methodand processing steps and/or operations, as described herein. Theprocessing component 2110 may include a noise filtering module 2112adapted to implement a noise reduction and/or removal algorithm (e.g., anoise filtering algorithm, such as any of those discussed herein). Inone aspect, the processing component 2110 may be adapted to performvarious other image processing algorithms including scaling the infraredimage data, either as part of or separate from the noise filteringalgorithm.

It should be appreciated that noise filtering module 2112 may beintegrated in software and/or hardware as part of the processingcomponent 2110, with code (e.g., software or configuration data) for thenoise filtering module 2112 stored, e.g., in the memory component 2120.Embodiments of the noise filtering algorithm, as disclosed herein, maybe stored by a separate computer-readable medium (e.g., a memory, suchas a hard drive, a compact disk, a digital video disk, or a flashmemory) to be executed by a computer (e.g., a logic or processor-basedsystem) to perform various methods and operations disclosed herein. Inone aspect, the computer-readable medium may be portable and/or locatedseparate from the system 2100, with the stored noise filtering algorithmprovided to the system 2100 by coupling the computer-readable medium tothe system 2100 and/or by the system 2100 downloading (e.g., via a wiredlink and/or a wireless link) the noise filtering algorithm from thecomputer-readable medium.

The memory component 2120 comprises, in one embodiment, one or morememory devices adapted to store data and information, including infrareddata and information. The memory device 2120 may comprise one or morevarious types of memory devices including volatile and non-volatilememory devices, such as RAM (Random Access Memory), ROM (Read-OnlyMemory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory,etc. The processing component 2110 may be adapted to execute softwarestored in the memory component 2120 so as to perform method and processsteps and/or operations described herein.

The image capture component 2130 comprises, in one embodiment, one ormore infrared sensors (e.g., any type of multi-pixel infrared detector,such as a focal plane array) for capturing infrared image data (e.g.,still image data and/or video data) representative of an image, such asscene 2170. In one implementation, the infrared sensors of the imagecapture component 2130 provide for representing (e.g., converting) thecaptured image data as digital data (e.g., via an analog-to-digitalconverter included as part of the infrared sensor or separate from theinfrared sensor as part of the system 2100). In one aspect, the infraredimage data (e.g., infrared video data) may comprise non-uniform data(e.g., real image data) of an image, such as scene 2170. The processingcomponent 2110 may be adapted to process the infrared image data (e.g.,to provide processed image data), store the infrared image data in thememory component 2120, and/or retrieve stored infrared image data fromthe memory component 2120. For example, the processing component 2110may be adapted to process infrared image data stored in the memorycomponent 2120 to provide processed image data and information (e.g.,captured and/or processed infrared image data).

The control component 2140 comprises, in one embodiment, a user inputand/or interface device, such as a rotatable knob (e.g., potentiometer),push buttons, slide bar, keyboard, etc., that is adapted to generate auser input control signal. The processing component 2110 may be adaptedto sense control input signals from a user via the control component2140 and respond to any sensed control input signals received therefrom.The processing component 2110 may be adapted to interpret such a controlinput signal as a value, as generally understood by one skilled in theart.

In one embodiment, the control component 2140 may comprise a controlunit (e.g., a wired or wireless handheld control unit) having pushbuttons adapted to interface with a user and receive user input controlvalues. In one implementation, the push buttons of the control unit maybe used to control various functions of the system 2100, such asautofocus, menu enable and selection, field of view, brightness,contrast, noise filtering, high pass filtering, low pass filtering,and/or various other features as understood by one skilled in the art.In another implementation, one or more of the push buttons may be usedto provide input values (e.g., one or more noise filter values,adjustment parameters, characteristics, etc.) for a noise filteralgorithm. For example, one or more push buttons may be used to adjustnoise filtering characteristics of infrared images captured and/orprocessed by the system 2100.

The display component 2150 comprises, in one embodiment, an imagedisplay device (e.g., a liquid crystal display (LCD)) or various othertypes of generally known video displays or monitors. The processingcomponent 2110 may be adapted to display image data and information onthe display component 2150. The processing component 2110 may be adaptedto retrieve image data and information from the memory component 2120and display any retrieved image data and information on the displaycomponent 2150. The display component 2150 may comprise displayelectronics, which may be utilized by the processing component 2110 todisplay image data and information (e.g., infrared images). The displaycomponent 2150 may be adapted to receive image data and informationdirectly from the image capture component 2130 via the processingcomponent 2110, or the image data and information may be transferredfrom the memory component 2120 via the processing component 2110.

The optional sensing component 2160 comprises, in one embodiment, one ormore sensors of various types, depending on the application orimplementation requirements, as would be understood by one skilled inthe art. The sensors of the optional sensing component 2160 provide dataand/or information to at least the processing component 2110. In oneaspect, the processing component 2110 may be adapted to communicate withthe sensing component 2160 (e.g., by receiving sensor information fromthe sensing component 2160) and with the image capture component 2130(e.g., by receiving data and information from the image capturecomponent 2130 and providing and/or receiving command, control, and/orother information to and/or from one or more other components of thesystem 2100).

In various implementations, the sensing component 2160 may provideinformation regarding environmental conditions, such as outsidetemperature, lighting conditions (e.g., day, night, dusk, and/or dawn),humidity level, specific weather conditions (e.g., sun, rain, and/orsnow), distance (e.g., laser rangefinder), and/or whether a tunnel orother type of enclosure has been entered or exited. The sensingcomponent 2160 may represent conventional sensors as generally known byone skilled in the art for monitoring various conditions (e.g.,environmental conditions) that may have an effect (e.g., on the imageappearance) on the data provided by the image capture component 2130.

In some implementations, the optional sensing component 2160 (e.g., oneor more of sensors) may comprise devices that relay information to theprocessing component 2110 via wired and/or wireless communication. Forexample, the optional sensing component 2160 may be adapted to receiveinformation from a satellite, through a local broadcast (e.g., radiofrequency (RF)) transmission, through a mobile or cellular networkand/or through information beacons in an infrastructure (e.g., atransportation or highway information beacon infrastructure), or variousother wired and/or wireless techniques.

In various embodiments, components of the system 2100 may be combinedand/or implemented or not, as desired or depending on the application orrequirements, with the system 2100 representing various functionalblocks of a related system. In one example, the processing component2110 may be combined with the memory component 2120, the image capturecomponent 2130, the display component 2150, and/or the optional sensingcomponent 2160. In another example, the processing component 2110 may becombined with the image capture component 2130 with only certainfunctions of the processing component 2110 performed by circuitry (e.g.,a processor, a microprocessor, a logic device, a microcontroller, etc.)within the image capture component 2130. Furthermore, various componentsof the system 2100 may be remote from each other (e.g., image capturecomponent 2130 may comprise a remote sensor with processing component2110, etc. representing a computer that may or may not be incommunication with the image capture component 2130).

In accordance with an embodiment of the disclosure, FIG. 15A shows amethod 2220 for noise filtering an infrared image. In oneimplementation, this method 2220 relates to the reduction and/or removalof temporal, 1/f, and/or fixed spatial noise in infrared imagingdevices, such as infrared imaging system 2100 of FIG. 14. The method2220 is adapted to utilize the row and column based noise components ofinfrared image data in a noise filtering algorithm. In one aspect, therow and column based noise components may dominate the noise in imageryof infrared sensors (e.g., approximately ⅔ of the total noise may bespatial in a typical micro-bolometer based system).

In one embodiment, the method 2220 of FIG. 15A comprises a high levelblock diagram of row and column noise filtering algorithms. In oneaspect, the row and column noise filter algorithms may be optimized touse minimal hardware resources.

Referring to FIG. 15A, the process flow of the method 2220 implements arecursive mode of operation, wherein the previous correction terms areapplied before calculating row and column noise, which may allow forcorrection of lower spatial frequencies. In one aspect, the recursiveapproach is useful when row and column noise is spatially correlated.This is sometimes referred to as banding and, in the column noise case,may manifest as several neighboring columns being affected by a similaroffset error. When several neighbors used in difference calculations aresubject to similar error, the mean difference used to calculate theerror may be skewed, and the error may only be partially corrected. Byapplying partial correction prior to calculating the error in thecurrent frame, correction of the error may be recursively reduced untilthe error is minimized or eliminated. In the recursive case, if the HPFis not applied (block 2208), then natural gradients as part of the imagemay, after several iterations, be distorted when merged into the noisemodel. In one aspect, a natural horizontal gradient may appear as lowspatially correlated column noise (e.g., severe banding). In anotheraspect, the HPF may prevent very low frequency scene information tointerfere with the noise estimate and, therefore, limits the negativeeffects of recursive filtering.

Referring to method 2220 of FIG. 15A, infrared image data (e.g., a rawvideo source, such as from the image capture component 2130 of FIG. 14)is received as input video data (block 2200). Next, column correctionterms are applied to the input video data (block 2201), and rowcorrection terms are applied to the input video data (block 2202). Next,video data (e.g., “cleaned” video data) is provided as output video data(2219) after column and row corrections are applied to the input videodata. In one aspect, the term “cleaned” may refer to removing orreducing noise (blocks 2201, 2202) from the input video data via, e.g.,one or more embodiments of the noise filter algorithm.

Referring to the processing portion (e.g., recursive processing) of FIG.15A, a HPF is applied (block 2208) to the output video data 2219 viadata signal path 2219 a. In one implementation, the high pass filtereddata is separately provided to a column noise filter portion 2201 a anda row noise filter portion 2202 a.

Referring to the column noise filter portion 2201 a, the method 2220 maybe adapted to process the input video data 2200 and/or output video data2219 as follows:

1. Apply previous column noise correction terms to a current frame ascalculated in a previous frame (block 2201).

2. High pass filter the row of the current frame by subtracting theresult of a low pass filter (LPF) operation (block 2208), for example,as discussed in reference to FIGS. 16A-16C.

3. For each pixel, calculate a difference between a center pixel and oneor more (e.g., eight) nearest neighbors (block 2214). In oneimplementation, the nearest neighbors comprise one or more nearesthorizontal neighbors. The nearest neighbors may include one or morevertical or other non-horizontal neighbors (e.g., not pure horizontal,i.e., on the same row), without departing from the scope of theinvention.

4. If the calculated difference is below a predefined threshold, add thecalculated difference to a histogram of differences for the specificcolumn (block 2209).

5. At an end of the current frame, find a median difference by examininga cumulative histogram of differences (block 2210). In one aspect, foradded robustness, only differences with some specified minimum number ofoccurrences may be used.

6. Delay the current correction terms for one frame (block 2211), i.e.,they are applied to the next frame.

7. Add median difference (block 2212) to previous column correctionterms to provide updated column correction terms (block 2213).

8. Apply updated column noise correction terms in the next frame (block2201).

Referring to the row noise filter portion 2202 a, the method 2220 may beadapted to process the input video data 2200 and/or output video data2219 as follows:

1. Apply previous row noise correction terms to a current frame ascalculated in a previous frame (block 2202).

2. High pass filter the column of the current frame by subtracting theresult of a low pass filter (LPF) operation (block 2208), as discussedsimilarly above for column noise filter portion 2201 a.

3. For each pixel, calculate a difference between a center pixel and oneor more (e.g., eight) nearest neighbors (block 2215). In oneimplementation, the nearest neighbors comprise one or more nearestvertical neighbors. The nearest neighbors may include one or morehorizontal or other non-vertical neighbors (e.g., not pure vertical,i.e., on the same column), without departing from the scope of theinvention.

4. If the calculated difference is below a predefined threshold, add thecalculated difference to a histogram of differences for the specific row(block 2207).

5. At an end of the current row (e.g., line), find a median differenceby examining a cumulative histogram of differences (block 2206). In oneaspect, for added robustness only differences with some specifiedminimum number of occurrences may be used.

6. Delay the current frame by a time period equivalent to the number ofnearest vertical neighbors used, for example eight.

7. Add median difference (block 2204) to row correction terms (block2203) from previous frame (block 2205).

8. Apply updated row noise correction terms in the current frame (block2202). In one aspect, this may require a row buffer (e.g., as mentionedin 6).

In one aspect, for all pixels (or at least a large subset of them) ineach column, an identical offset term (or set of terms) may be appliedfor each associated column. This may prevent the filter from blurringspatially local details.

Similarly, in one aspect, for all pixels (or at least a large subset ofthem) in each row respectively, an identical offset term (or set ofterms) may be applied. This may inhibit the filter from blurringspatially local details.

In one example, an estimate of the column offset terms may be calculatedusing only a subset of the rows (e.g., the first 32 rows). In this case,only a 32 row delay is needed to apply the column correction terms inthe current frame. This may improve filter performance in removing hightemporal frequency column noise. Alternatively, the filter may bedesigned with minimum delay, and the correction terms are only appliedonce a reasonable estimate can be calculated (e.g., using data from the32 rows). In this case, only rows 33 and beyond may be optimallyfiltered.

In one aspect, all samples may not be needed, and in such an instance,only every 2nd or 4th row, e.g., may be used for calculating the columnnoise. In another aspect, the same may apply when calculating row noise,and in such an instance, only data from every 4th column, e.g., may beused. It should be appreciated that various other iterations may be usedby one skilled in the art without departing from the scope of theinvention.

In one aspect, the filter may operate in recursive mode in which thefiltered data is filtered instead of the raw data being filtered. Inanother aspect, the mean difference between a pixel in one row andpixels in neighboring rows may be approximated in an efficient way if arecursive (IIR) filter is used to calculate an estimated running mean.For example, instead of taking the mean of neighbor differences (e.g.,eight neighbor differences), the difference between a pixel and the meanof the neighbors may be calculated.

In accordance with an embodiment of the disclosure, FIG. 15B shows analternative method 2230 for noise filtering infrared image data. Inreference to FIGS. 15A and 15B, one or more of the process steps and/oroperations of method 2220 of FIG. 15A have changed order or have beenaltered or combined for the method 2230 of FIG. 15B. For example, theoperation of calculating row and column neighbor differences (blocks2214, 2215) may be removed or combined with other operations, such asgenerating histograms of row and column neighbor differences (blocks2207, 2209). In another example, the delay operation (block 2205) may beperformed after finding the median difference (block 2206). In variousexamples, it should be appreciated that similar process steps and/oroperations have similar scope, as previously described in FIG. 15A, andtherefore, the description will not be repeated.

In still other alternate approaches to methods 2220 and 2230,embodiments may exclude the histograms and rely on mean calculateddifferences instead of median calculated differences. In one aspect,this may be slightly less robust but may allow for a simplerimplementation of the column and row noise filters. For example, themean of neighboring rows and columns, respectively, may be approximatedby a running mean implemented as an infinite impulse response (IIR)filter. In the row noise case, the IIR filter implementation may reduceor even eliminate the need to buffer several rows of data for meancalculations.

In still other alternate approaches to methods 2220 and 2230, new noiseestimates may be calculated in each frame of the video data and onlyapplied in the next frame (e.g., after noise estimates). In one aspect,this alternate approach may provide less performance but may be easierto implement. In another aspect, this alternate approach may be referredto as a non-recursive method, as understood by those skilled in the art.

For example, in one embodiment, the method 2240 of FIG. 15C comprises ahigh level block diagram of row and column noise filtering algorithms.In one aspect, the row and column noise filter algorithms may beoptimized to use minimal hardware resources. In reference to FIGS. 15Aand 15B, similar process steps and/or operations may have similar scope,and therefore, the descriptions will not be repeated.

Referring to FIG. 15C, the process flow of the method 2240 implements anon-recursive mode of operation. As shown, the method 2240 appliescolumn offset correction term 2201 and row offset correction term 2202to the uncorrected input video data from video source 2200 to produce,e.g., a corrected or cleaned output video signal 2219. In column noisefilter portion 2201 a, column offset correction terms 2213 arecalculated based on the mean difference 2210 between pixel values in aspecific column and one or more pixels belonging to neighboring columns2214. In row noise filter portion 2202 a, row offset correction terms2203 are calculated based on the mean difference 2206 between pixelvalues in a specific row and one or more pixels belonging to neighboringrows 2215. In one aspect, the order (e.g., rows first or columns first)in which row or column offset correction terms 2203, 2213 are applied tothe input video data from video source 2200 may be considered arbitrary.In another aspect, the row and column correction terms may not be fullyknown until the end of the video frame, and therefore, if the inputvideo data from the video source 2200 is not delayed, the row and columncorrection terms 2203, 2213 may not be applied to the input video datafrom which they were calculated.

In one aspect of the invention, the column and row noise filteralgorithm may operate continuously on image data provided by an infraredimaging sensor (e.g., image capture component 2130 of FIG. 14). Unlikeconventional methods that may require a uniform scene (e.g., as providedby a shutter or external calibrated black body) to estimate the spatialnoise, the column and row noise filter algorithms, as set forth in oneor more embodiments, may operate on real-time scene data. In one aspect,an assumption may be made that, for some small neighborhood aroundlocation [x, y], neighboring infrared sensor elements should providesimilar values since they are imaging parts of the scene in closeproximity. If the infrared sensor reading from a particular infraredsensor element differs from a neighbor, then this could be the result ofspatial noise. However, in some instances, this may not be true for eachand every sensor element in a particular row or column (e.g., due tolocal gradients that are a natural part of the scene), but on average, arow or column may have values that are close to the values of theneighboring rows and columns.

For one or more embodiments, by first taking out one or more low spatialfrequencies (e.g., using a high pass filter (HPF)), the scenecontribution may be minimized to leave differences that correlate highlywith actual row and column spatial noise. In one aspect, by using anedge preserving filter, such as a Median filter or a Bilateral filter,one or more embodiments may minimize artifacts due to strong edges inthe image.

In accordance with one or more embodiments of the disclosure, FIGS. 16Ato 16C show a graphical implementation (e.g., digital counts versus datacolumns) of filtering an infrared image. FIG. 16A shows a graphicalillustration (e.g., graph 2300) of typical values, as an example, from arow of sensor elements when imaging a scene. FIG. 16B shows a graphicalillustration (e.g., graph 2310) of a result of a low pass filtering(LPF) of the image data values from FIG. 16A. FIG. 16C shows a graphicalillustration (e.g., graph 2320) of subtracting the low pass filter (LPF)output in FIG. 16B from the original image data in FIG. 16A, whichresults in a high pass filter (HPF) profile with low and mid frequencycomponents removed from the scene of the original image data in FIG.16A. Thus, FIG. 16A-16C illustrate a HPF technique, which may be usedfor one or more embodiments (e.g., as with methods 2220 and/or 2230).

In one aspect of the invention, a final estimate of column and/or rownoise may be referred to as an average or median estimate of all of themeasured differences. Because noise characteristics of an infraredsensor are often generally known, then one or more thresholds may beapplied to the noise estimates. For example, if a difference of 60digital counts is measured, but it is known that the noise typically isless than 10 digital counts, then this measurement may be ignored.

In accordance with one or more embodiments of the disclosure, FIG. 17shows a graphical illustration 2400 (e.g., digital counts versus datacolumns) of a row of sensor data 2401 (e.g., a row of pixel data for aplurality of pixels in a row) with column 5 data 2402 and data for eightnearest neighbors (e.g., nearest pixel neighbors, 4 columns 2410 to theleft of column 5 data 2402 and 4 columns 2411 to the right of column 5data 2402). In one aspect, referring to FIG. 17, the row of sensor data2401 is part of a row of sensor data for an image or scene captured by amulti-pixel infrared sensor or detector (e.g., image capture component2130 of FIG. 14). In one aspect, column 5 data 2402 is a column of datato be corrected. For this row of sensor data 2401, the differencebetween column 5 data 2402 and a mean 2403 of its neighbor columns(2410, 2411) is indicated by an arrow 2404. Therefore, noise estimatesmay be obtained and accounted for based on neighboring data.

In accordance with one or more embodiments of the disclosure, FIGS. 18Ato 18C show an exemplary implementation of column and row noisefiltering an infrared image (e.g., an image frame from infrared videodata). FIG. 18A shows an infrared image 2500 with column noise estimatedfrom a scene with severe row and column noise present and acorresponding graph 2502 of column correction term's. FIG. 18B shows aninfrared image 2510, with column noise removed and spatial row noisestill present, with row correction terms estimated from the scene inFIG. 18A and a corresponding graph 2512 of row correction terms. FIG.18C shows an infrared image 2520 of the scene in FIG. 18A as a cleanedinfrared image with row and column noise removed (e.g., column and rowcorrection terms of FIGS. 18A-18B applied).

In one embodiment, FIG. 18A shows an infrared video frame (i.e.,infrared image 2500) with severe row and column noise. Column noisecorrection coefficients are calculated as described herein to produce,e.g., 639 correction terms, i.e., one correction term per column. Thegraph 2502 shows the column correction terms. These offset correctionterms are subtracted from the infrared video frame 2500 of FIG. 18A toproduce the infrared image 2510 in FIG. 18B. As shown in FIG. 18B, therow noise is still present. Row noise correction coefficients arecalculated as described herein to produce, e.g., 639 row terms, i.e.,one correction term per row. The graph 2512 shows the row offsetcorrection terms, which are subtracted from the infrared image 2510 inFIG. 18B to produce the cleaned infrared image 2520 in FIG. 18C withsignificantly reduced or removed row and column noise.

In various embodiments, it should be understood that both row and columnfiltering is not required. For example, either column noise filtering2201 a or row noise filtering 2202 a may be performed in methods 2220,2230 or 2240.

It should be appreciated that any reference to a column or a row mayinclude a partial column or a partial row and that the terms “row” and“column” are interchangeable and not limiting. For example, withoutdeparting from the scope of the invention, the term “row” may be used todescribe a row or a column, and likewise, the term “column” may be usedto describe a row or a column, depending upon the application.

In various aspects, column and row noise may be estimated by looking ata real scene (e.g., not a shutter or a black body), in accordance withembodiments of the noise filtering algorithms, as disclosed herein. Thecolumn and row noise may be estimated by measuring the median or meandifference between sensor readings from elements in a specific row(and/or column) and sensor readings from adjacent rows (and/or columns).

Optionally, a high pass filter may be applied to the image data prior tomeasuring the differences, which may reduce or at least minimize a riskof distorting gradients that are part of the scene and/or introducingartifacts. In one aspect, only sensor readings that differ by less thana configurable threshold may be used in the mean or median estimation.Optionally, a histogram may be used to effectively estimate the median.Optionally, only histogram bins exceeding a minimum count may be usedwhen finding the median estimate from the histogram. Optionally, arecursive IIR filter may be used to estimate the difference between apixel and its neighbors, which may reduce or at least minimize the needto store image data for processing, e.g., the row noise portion (e.g.,if image data is read out row wise from the sensor). In oneimplementation, the current mean column value C _(i,j) for column i atrow j may be estimated using the following recursive filter algorithm.

${\overset{\_}{C}}_{i,j} = {{( {1 - \alpha} ) \cdot {\overset{\_}{C}}_{{i - 1},j}} + {\alpha \cdot C_{i,j}}}$${\Delta\; R_{i}} = {{\frac{1}{N}{\sum\limits_{j = 1}^{N}\; C_{i,j}}} - {\overset{\_}{C}}_{{i - 1},j}}$

In this equation a is the damping factor and may be set to for example0.2 in which case the estimate for the running mean of a specific columni at row j will be a weighted sum of the estimated running mean forcolumn i−1 at row j and the current pixel value at row j and column i.The estimated difference between values of row j and the values ofneighboring rows can now be approximated by taking the difference ofeach value C_(i,j) and the running recursive mean of the neighbors aboverow i (C _(i-1,j)). Estimating the mean difference this way is not asaccurate as taking the true mean difference since only rows above areused but it requires that only one row of running means are stored ascompared to several rows of actual pixel values be stored.

In one embodiment, referring to FIG. 15A, the process flow of method2220 may implement a recursive mode of operation, wherein the previouscolumn and row correction terms are applied before calculating row andcolumn noise, which allows for correction of lower spatial frequencieswhen the image is high pass filtered prior to estimating the noise.

Generally, during processing, a recursive filter re-uses at least aportion of the output data as input data. The feedback input of therecursive filter may be referred to as an infinite impulse response(IIR), which may be characterized, e.g., by exponentially growing outputdata, exponentially decaying output data, or sinusoidal output data. Insome implementations, a recursive filter may not have an infiniteimpulse response. As such, e.g., some implementations of a movingaverage filter function as recursive filters but with a finite impulseresponse (FIR).

As further set forth in the description of FIGS. 19A to 22B, additionaltechniques are contemplated to determine row and/or column correctionterms. For example, in some embodiments, such techniques may be used toprovide correction terms without overcompensating for the presence ofvertical and/or horizontal objects present in scene 2170. Suchtechniques may be used in any appropriate environment where such objectsmay be frequently captured including, for example, urban applications,rural applications, vehicle applications, and others. In someembodiments, such techniques may provide correction terms with reducedmemory and/or reduced processing overhead in comparison with otherapproaches used to determine correction terms.

FIG. 19A shows an infrared image 2600 (e.g., infrared image data) ofscene 2170 in accordance with an embodiment of the disclosure. Althoughinfrared image 2600 is depicted as having 16 rows and 16 columns, otherimage sizes are contemplated for infrared image 2600 and the variousother infrared images discussed herein. For example, in one embodiment,infrared image 2600 may have 640 columns and 512 rows.

In FIG. 19A, infrared image 2600 depicts scene 2170 as relativelyuniform, with a majority of pixels 2610 of infrared image 2600 havingthe same or similar intensity (e.g., the same or similar numbers ofdigital counts). Also in this embodiment, scene 2170 includes an object2621 which appears in pixels 2622A-D of a column 2620A of infrared image2600. In this regard, pixels 2622A-D are depicted somewhat darker thanother pixels 2610 of infrared image 2600. For purposes of discussion, itwill be assumed that darker pixels are associated with higher numbers ofdigital counts, however lighter pixels may be associated with highernumbers of digital counts in other implementations if desired. As shown,the remaining pixels 2624 of column 2620A have a substantially uniformintensity with pixels 2610.

In some embodiments, object 2621 may be a vertical object such as abuilding, telephone pole, light pole, power line, cellular tower, tree,human being, and/or other object. If image capture component 2130 isdisposed in a vehicle approaching object 2621, then object 2621 mayappear relatively fixed in infrared image 2600 while the vehicle isstill sufficiently far away from object 2621 (e.g., object 2621 mayremain primarily represented by pixels 2622A-D and may not significantlyshift position within infrared image 2600). If image capture component2130 is disposed at a fixed location relative to object 2621, thenobject 2621 may also appear relatively fixed in infrared image 2600(e.g., if object 2621 is fixed and/or is positioned sufficiently faraway). Other dispositions of image capture component 2130 relative toobject 2621 are also contemplated.

Infrared image 2600 also includes another pixel 2630 which may beattributable to, for example, temporal noise, fixed spatial noise, afaulty sensor/circuitry, actual scene information, and/or other sources.As shown in FIG. 19A, pixel 2630 is darker (e.g., has a higher number ofdigital counts) than all of pixels 2610 and 2622A-D.

Vertical objects such as object 2621 depicted by pixels 2622A-D areoften problematic for some column correction techniques. In this regard,objects that remain disposed primarily in one or several columns mayresult in overcompensation when column correction terms are calculatedwithout regard to the possible presence of small vertical objectsappearing in scene 2170. For example, when pixels 2622A-D of column2620A are compared with those of nearby columns 2620B-E, some columncorrection techniques may interpret pixels 2622A-D as column noise,rather than actual scene information. Indeed, the significantly darkerappearance of pixels 2622A-D relative to pixels 2610 and the relativelysmall width of object 2621 disposed in column 2620A may skew thecalculation of a column correction term to significantly correct theentire column 2620A, although only a small portion of column 2620Aactually includes darker scene information. As a result, the columncorrection term determined for column 2620A may significantly lighten(e.g., brighten or reduce the number of digital counts) column 2620A tocompensate for the assumed column noise.

For example, FIG. 19B shows a corrected version 2650 of infrared image2600 of FIG. 19A. As shown in FIG. 19B, column 2620A has beensignificantly brightened. Pixels 2622A-D have been made significantlylighter to be approximately uniform with pixels 2610, and the actualscene information (e.g., the depiction of object 2621) contained inpixels 2622A-D has been mostly lost. In addition, remaining pixels 2624of column 2620A have been significantly brightened such that they are nolonger substantially uniform with pixels 2610. Indeed, the columncorrection term applied to column 2620A has actually introduced newnon-uniformities in pixels 2624 relative to the rest of scene 2170.

Various techniques described herein may be used to determine columncorrection terms without overcompensating for the appearance of variousvertical objects that may be present in scene 2170. For example, in oneembodiment, when such techniques are applied to column 2620A of FIG.19A, the presence of dark pixels 2622A-D may not cause any furtherchanges to the column correction term for column 2620A (e.g., aftercorrection is applied, column 2620A may appear as shown in FIG. 19Arather than as shown in FIG. 19B).

In accordance with various embodiments further described herein,corresponding column correction terms may be determined for each columnof an infrared image without overcompensating for the presence ofvertical objects present in scene 2170. In this regard, a first pixel ofa selected column of an infrared image (e.g., the pixel of the columnresiding in a particular row) may be compared with a corresponding setof other pixels (e.g., also referred to as neighborhood pixels) that arewithin a neighborhood associated with the first pixel. In someembodiments, the neighborhood may correspond to pixels in the same rowas the first pixel that are within a range of columns. For example, theneighborhood may be defined by an intersection of: the same row as thefirst pixel; and a predetermined range of columns.

The range of columns may be any desired number of columns on the leftside, right side, or both left and right sides of the selected column.In this regard, if the range of columns corresponds to two columns onboth sides of the selected column, then four comparisons may be made forthe first pixel (e.g., two columns to the left of the selected column,and two columns to the right of the selected column). Although a rangeof two columns on both sides of the selected column is further describedherein, other ranges are also contemplated (e.g., 5 columns, 8 columns,or any desired number of columns).

One or more counters (e.g., registers, memory locations, accumulators,and/or other implementations in processing component 2110, noisefiltering module 2112, memory component 2120, and/or other components)are adjusted (e.g., incremented, decremented, or otherwise updated)based on the comparisons. In this regard, for each comparison where thepixel of the selected column has a lesser value than a compared pixel, acounter A may be adjusted. For each comparison where the pixel of theselected column has an equal (e.g., exactly equal or substantiallyequal) value as a compared pixel, a counter B may be adjusted. For eachcomparison where the pixel of the selected column has a greater valuethan a compared pixel, a counter C may be adjusted. Thus, if the rangeof columns corresponds to two columns on either side of the selectedcolumn as identified in the example above, then a total of fouradjustments (e.g., counts) may be collectively held by counters A, B,and C.

After the first pixel of the selected column is compared with all pixelsin its corresponding neighborhood, the process is repeated for allremaining pixels in the selected column (e.g., one pixel for each row ofthe infrared image), and counters A, B, and C continue to be adjusted inresponse to the comparisons performed for the remaining pixels. In thisregard, in some embodiments, each pixel of the selected column may becompared with a different corresponding neighborhood of pixels (e.g.,pixels residing: in the same row as the pixel of the selected column;and within a range of columns), and counters A, B, and C may be adjustedbased on the results of such comparisons.

As a result, after all pixels of the selected column are compared,counters A, B, and C may identify the number of comparisons for whichpixels of the selected column were found to be greater, equal, or lessthan neighborhood pixels. Thus, continuing the example above, if theinfrared image has 16 rows, then a total of 64 counts may be distributedacross counters A, B, and C for the selected column (e.g., 4 counts perrow×16 rows=64 counts). It is contemplated that other numbers of countsmay be used. For example, in a large array having 512 rows and using arange of 10 columns, 5120 counts (e.g., 512 rows×10 columns) may be usedto determine each column correction term.

Based on the distribution of the counts in counters A, B, and C, thecolumn correction term for the selected column may be selectivelyincremented, decremented, or remain the same based on one or morecalculations performed using values of one or more of counters A, B,and/or C. For example, in some embodiments: the column correction termmay be incremented if counter A−counter B−counter C>D; the columncorrection term may be decremented if counter C−counter A−counter B>D;and the column correction term may remain the same in all other cases.In such embodiments, D may be a value such as a constant value smallerthan the total number of comparisons accumulated by counters A, B, and Cper column. For example, in one embodiment, D may have a value equal to:(number of rows)/2.

The process may be repeated for all remaining columns of the infraredimage in order to determine (e.g., calculate and/or update) acorresponding column correction term for each column of the infraredimage. In addition, after column correction terms have been determinedfor one or more columns, the process may be repeated for one or morecolumns (e.g., to increment, decrement, or not change one or more columncorrection terms) after the column corrected terms are applied to thesame infrared image and/or another infrared image (e.g., a subsequentlycaptured infrared image).

As discussed, counters A, B, and C identify the number of comparedpixels that are less than, equal to, or greater than pixels of theselected column. This contrasts with various other techniques used todetermine column correction terms where the actual differences (e.g.,calculated difference values) between compared pixels may be used.

By determining column correction terms based on less than, equal to, orgreater than relationships (e.g., rather than the actual numericaldifferences between the digital counts of different pixels), the columncorrection terms may be less skewed by the presence of small verticalobjects appearing in infrared images. In this regard, by using thisapproach, small objects such as object 2621 with high numbers of digitalcounts may not inadvertently cause column correction terms to becalculated that would overcompensate for such objects (e.g., resultingin an undesirable infrared image 2650 as shown in FIG. 19B). Rather,using this approach, object 2621 may not cause any change to columncorrection terms (e.g., resulting in an unchanged infrared image 2600 asshown in FIG. 19A). However, larger objects such as object 2721 whichmay be legitimately identified as column noise may be appropriatelyreduced through adjustment of column correction terms (e.g., resultingin a corrected infrared image 2750 as shown in FIG. 20B).

In addition, using this approach may reduce the effects of other typesof scene information on column correction term values. In this regard,counters A, B, and C identify relative relationships (e.g., less than,equal to, or greater than relationships) between pixels of the selectedcolumn and neighborhood pixels. In some embodiments, such relativerelationships may correspond, for example, to the sign (e.g., positive,negative, or zero) of the difference between the values of pixels of theselected column and the values of neighborhood pixels. By using relativerelationships rather than actual numerical differences, exponentialscene changes (e.g., non-linear scene information gradients) maycontribute less to column correction term determinations. For example,exponentially higher digital counts in certain pixels may be treated assimply being greater than or less than other pixels for comparisonpurposes and consequently will not unduly skew the column correctionterm.

In addition, by identifying relative relationships rather than actualnumerical differences in counters A, B, and C, high pass filtering canbe reduced in some embodiments. In this regard, where low frequencyscene information or noise remains fairly uniform throughout comparedneighborhoods of pixels, such low frequency content may notsignificantly affect the relative relationships between the comparedpixels.

Advantageously, counters A, B, and C provide an efficient approach tocalculating column correction terms. In this regard, in someembodiments, only three counters A, B, and C are used to store theresults of all pixel comparisons performed for a selected column. Thiscontrasts with various other approaches in which many more unique valuesare stored (e.g., where particular numerical differences, or the numberof occurrences of such numerical differences, are stored).

In some embodiments, where the total number of rows of an infrared imageis known, further efficiency may be achieved by omitting counter B. Inthis regard, the total number of counts may be known based on the rangeof columns used for comparison and the number of rows of the infraredimage. In addition, it may be assumed that any comparisons that do notresult in counter A or counter C being adjusted will correspond to thosecomparisons where pixels have equal values. Therefore, the value thatwould have been held by counter B may be determined from counters A andC (e.g., (number of rows×range)−counter A value−counter B value=counterC value).

In some embodiments, only a single counter may be used. In this regard,a single counter may be selectively adjusted in a first manner (e.g.,incremented or decremented) for each comparison where the pixel of theselected column has a greater value than a compared pixel, selectivelyadjusted in a second manner (e.g., decremented or incremented) for eachcomparison where the pixel of the selected column has a lesser valuethan a compared pixel, and not adjusted (e.g., retaining its existingvalue) for each comparison where the pixel of the selected column has anequal (e.g., exactly equal or substantially equal) value as a comparedpixel. Thus, the value of the single counter may indicate relativenumbers of compared pixels that are greater than or less than the pixelsof the selected column (e.g., after all pixels of the selected columnhave been compared with corresponding neighborhood pixels).

A column correction term for the selected column may be updated (e.g.,incremented, decremented, or remain the same) based on the value of thesingle counter. For example, in some embodiments, if the single counterexhibits a baseline value (e.g., zero or other number) after comparisonsare performed, then the column correction term may remain the same. Insome embodiments, if the single counter is greater or less than thebaseline value, the column correction term may be selectivelyincremented or decremented as appropriate to reduce the overalldifferences between the compared pixels and the pixels of the selectedcolumn. In some embodiments, the updating of the column correction termmay be conditioned on the single counter having a value that differsfrom the baseline value by at least a threshold amount to prevent undueskewing of the column correction term based on limited numbers ofcompared pixels having different values from the pixels of the selectedcolumn.

These techniques may also be used to compensate for larger verticalanomalies in infrared images where appropriate. For example, FIG. 20Aillustrates an infrared image 2700 of scene 2170 in accordance with anembodiment of the disclosure. Similar to infrared image 2600, infraredimage 2700 depicts scene 2170 as relatively uniform, with a majority ofpixels 2710 of infrared image 2700 having the same or similar intensity.Also in this embodiment, a column 2720A of infrared image 2700 includespixels 2722A-M that are somewhat darker than pixels 2710, while theremaining pixels 2724 of column 2720A have a substantially uniformintensity with pixels 2710.

However, in contrast to pixels 2622A-D of FIG. 19A, pixels 2722A-M ofFIG. 20A occupy a significant majority of column 2720A. As such, it ismore likely that an object 2721 depicted by pixels 2722A-M may actuallybe an anomaly such as column noise or another undesired source ratherthan an actual structure or other actual scene information. For example,in some embodiments, it is contemplated that actual scene informationthat occupies a significant majority of at least one column would alsolikely occupy a significant horizontal portion of one or more rows. Forexample, a vertical structure in close proximity to image capturecomponent 2130 may be expected to occupy multiple columns and/or rows ofinfrared image 2700. Because object 2721 appears as a tall narrow bandoccupying a significant majority of only one column 2720A, it is morelikely that object 2721 is actually column noise.

FIG. 20B shows a corrected version 2750 of infrared image 2700 of FIG.20A. As shown in FIG. 20B, column 2720A has been brightened, but not assignificantly as column 2620A of infrared image 2650. Pixels 2722A-Mhave been made lighter, but still appear slightly darker than pixels2710. In another embodiment, column 2720A may be corrected such thatpixels 2722A-M may be approximately uniform with pixels 2710. As alsoshown in FIG. 20B, remaining pixels 2724 of column 2720A have beenbrightened but not as significantly as pixels 2624 of infrared image2650. In another embodiment, pixels 2724 may be further brightened ormay remain substantially uniform with pixels 2710.

Various aspects of these techniques are further explained with regard toFIGS. 21 and 22A-B. In this regard, FIG. 21 is a flowchart illustratinga method 2800 for noise filtering an infrared image, in accordance withan embodiment of the disclosure. Although particular components ofsystem 2100 are referenced in relation to particular blocks of FIG. 21,the various operations described with regard to FIG. 21 may be performedby any appropriate components, such as image capture component 2130,processing component, 2110, noise filtering module 2112, memorycomponent 2120, control component 2140, and/or others.

In block 2802, image capture component 2130 captures an infrared image(e.g., infrared image 2600 or 2700) of scene 2170. In block 2804, noisefiltering module 2112 applies existing row and column correction termsto infrared image 2600/2700. In some embodiments, such existing row andcolumn correction terms may be determined by any of the varioustechniques described herein, factory calibration operations, and/orother appropriate techniques. In some embodiments, the column correctionterms applied in block 2804 may be undetermined (e.g., zero) during afirst iteration of block 2804, and may be determined and updated duringone or more iterations of FIG. 21.

In block 2806, noise filtering module 2112 selects a column of infraredimage 2600/2700. Although column 2620A/2720A will be referenced in thefollowing description, any desired column may be used. For example, insome embodiments, a rightmost or leftmost column of infrared image2600/2700 may be selected in a first iteration of block 2806. In someembodiments, block 2806 may also include resetting counters A, B, and Cto zero or another appropriate default value.

In block 2808, noise filtering module 2112 selects a row of infraredimage 2600/2700. For example, a topmost row 2601A/2701A of infraredimage 2600/2700 may be selected in a first iteration of block 2808.Other rows may be selected in other embodiments.

In block 2810, noise filtering module 2112 selects another column in aneighborhood for comparison to column 2620A. In this example, theneighborhood has a range of two columns (e.g., columns 2620B-E/2720B-E)on both sides of column 2620A/2720A, corresponding to pixels2602B-E/2702B-E in row 2601A/2701A on either side of pixel 2602A/2702A.Accordingly, in one embodiment, column 2620B/2720B may be selected inthis iteration of block 2810.

In block 2812, noise filtering module 2112 compares pixels 2602B/2702Bto pixel 2602A/2702A. In block 2814, counter A is adjusted if pixel2602A/2702A has a lower value than pixel 2602B/2702B. Counter B isadjusted if pixel 2602A/2702A has an equal value as pixel 2602B/2702B.Counter C is adjusted if pixel 2602A/2702A has a higher value than pixel2602B/2702B. In this example, pixel 2602A/2702A has an equal value aspixel 2602B/2702B. Accordingly, counter B will be adjusted, and countersA and C will not be adjusted in this iteration of block 2814.

In block 2816, if additional columns in the neighborhood remain to becompared (e.g., columns 2620C-E/2720C-E), then blocks 2810-2816 arerepeated to compare the remaining pixels of the neighborhood (e.g.,pixels 2602B-E/2702B-E residing in columns 2620C-E/2720C-E and in row2601A/2701A) to pixel 2602A/2702A. In FIGS. 19A/20A, pixel 2602A/2702Ahas an equal value as all of pixels 2602B-E/2702B-E. Accordingly, afterpixel 2602A/2702A has been compared with all pixels in its neighborhood,counter B will have been adjusted by four counts, and counters A and Cwill not have been adjusted.

In block 2818, if additional rows remain in infrared images 2600/2700(e.g., rows 2601B-P/2701B-P), then blocks 2808-2818 are repeated tocompare the remaining pixels of column 2620A/2720A with the remainingpixels of columns 2602B-E/2702B-E on a row by row basis as discussedabove.

Following block 2818, each of the 16 pixels of column 2620A/2720A willhave been compared to 4 pixels (e.g., pixels in columns 2620B-E residingin the same row as each compared pixel of column 2620A/2720A) for atotal of 64 comparisons. This results in 64 adjustments collectivelyshared by counters A, B, and C.

FIG. 22A shows the values of counters A, B, and C represented by ahistogram 2900 after all pixels of column 2620A have been compared tothe various neighborhoods of pixels included in columns 2620B-E, inaccordance with an embodiment of the disclosure. In this case, countersA, B, and C have values of 1, 48, and 15, respectively. Counter A wasadjusted only once as a result of pixel 2622A of column 2620A having alower value than pixel 2630 of column 2620B. Counter C was adjusted 15times as a result of pixels 2622A-D each having a higher value whencompared to their neighborhood pixels of columns 2620B-E (e.g., exceptfor pixel 2630 as noted above). Counter B was adjusted 48 times as aresult of the remaining pixels 2624 of column 2620A having equal valuesas the remaining neighborhood pixels of columns 2620B-E.

FIG. 22B shows the values of counters A, B, and C represented by ahistogram 2950 after all pixels of column 2720A have been compared tothe various neighborhoods of pixels included in columns 2720B-E, inaccordance with an embodiment of the disclosure. In this case, countersA, B, and C have values of 1, 12, and 51, respectively. Similar to FIG.22A, counter A in FIG. 22B was adjusted only once as a result of a pixel2722A of column 2720A having a lower value than pixel 2730 of column2720B. Counter C was adjusted 51 times as a result of pixels 2722A-Meach having a higher value when compared to their neighborhood pixels ofcolumns 2720B-E (e.g., except for pixel 2730 as noted above). Counter Bwas adjusted 12 times as a result of the remaining pixels of column2720A having equal values as the remaining neighborhood compared pixelsof columns 2720B-E.

Referring again to FIG. 21, in block 2820, the column correction termfor column 2620A/2720A is updated (e.g., selectively incremented,decremented, or remain the same) based on the values of counters A, B,and C. For example, as discussed above, in some embodiments, the columncorrection term may be incremented if counter A−counter B−counter C>D;the column correction term may be decremented if counter C−counterA−counter B>D; and the column correction term may remain the same in allother cases.

In the case of infrared image 2600, applying the above calculations tothe counter values identified in FIG. 22A results in no change to thecolumn correction term (e.g., 1 (counter A)−48 (counter B)−15 (counterC)=−62 which is not greater than D, where D equals (16 rows)/2; and 15(counter C)−1 (counter A)−48 (counter B)=−34 which is not greater thanD, where D equals (16 rows)/2). Thus, in this case, the values ofcounters A, B, and C, and the calculations performed thereon indicatethat values of pixels 2622A-D are associated with an actual object(e.g., object 2621) of scene 2170. Accordingly, the small verticalstructure 2621 represented by pixels 2622A-D will not result in anyovercompensation in the column correction term for column 2620A.

In the case of infrared image 2700, applying the above calculations tothe counter values identified in FIG. 22B results in a decrement in thecolumn correction term (e.g., 51 (counter C)−1 (counter A)−12 (counterB)=38 which is greater than D, where D equals (16 rows)/2). Thus, inthis case, the values of counters A, B, and C, and the calculationsperformed thereon indicate that the values of pixels 2722A-M areassociated with column noise. Accordingly, the large vertical object2721 represented by pixels 2722A-M will result in a lightening of column2720A to improve the uniformity of corrected infrared image 2750 shownin FIG. 20B.

At block 2822, if additional columns remain to have their columncorrection terms updated, then the process returns to block 2806 whereinblocks 2806-2822 are repeated to update the column correction term ofanother column. After all column correction terms have been updated, theprocess returns to block 2802 where another infrared image is captured.In this manner, FIG. 21 may be repeated to update column correctionterms for each newly captured infrared image.

In some embodiments, each newly captured infrared image may not differsubstantially from recent preceding infrared images. This may be due to,for example, a substantially static scene 2170, a slowing changing scene2170, temporal filtering of infrared images, and/or other reasons. Inthese cases, the accuracy of column correction terms determined by FIG.21 may improve as they are selectively incremented, decremented, orremain unchanged in each iteration of FIG. 21. As a result, in someembodiments, many of the column correction terms may eventually reach asubstantially steady state in which they remain relatively unchangedafter a sufficient number of iterations of FIG. 21, and while theinfrared images do not substantially change.

Other embodiments are also contemplated. For example, block 2820 may berepeated multiple times to update one or more column correction termsusing the same infrared image for each update. In this regard, after oneor more column correction terms are updated in block 2820, the processof FIG. 21 may return to block 2804 to apply the updated columncorrection terms to the same infrared image used to determine theupdated column correction terms. As a result, column correction termsmay be iteratively updated using the same infrared image. Such anapproach may be used, for example, in offline (non-realtime) processingand/or in realtime implementations with sufficient processingcapabilities.

In addition, any of the various techniques described with regard toFIGS. 19A-22B may be combined where appropriate with the othertechniques described herein. For example, some or all portions of thevarious techniques described herein may be combined as desired toperform noise filtering.

Although column correction terms have been primarily discussed withregard to FIGS. 19A-22B, the described techniques may be applied torow-based processing. For example, such techniques may be used todetermine and update row correction terms without overcompensating forsmall horizontal structures appearing in scene 2170, while alsoappropriately compensating for actual row noise. Such row-basedprocessing may be performed in addition to, or instead of variouscolumn-based processing described herein. For example, additionalimplementations of counters A, B, and/or C may be provided for suchrow-based processing.

In some embodiments where infrared images are read out on a row-by-rowbasis, row-corrected infrared images may be may be rapidly provided asrow correction terms are updated. Similarly, in some embodiments whereinfrared images are read out on a column-by-column basis,column-corrected infrared images may be may be rapidly provided ascolumn correction terms are updated.

Referring now to FIGS. 23A-E, as discussed, in some embodiments thetechniques described with regard to FIGS. 23A-E may be used in place ofand/or in addition to one or more operations of blocks 565-573 (seeFIGS. 5 and 8) to estimate FPN and/or determine NUC terms (e.g., flatfield correction terms). For example, in some embodiments, suchtechniques may be used to determine NUC terms to correct for spatiallycorrelated FPN and/or spatially uncorrelated (e.g., random) FPN withoutrequiring high pass filtering.

FIG. 23A illustrates an infrared image 3000 (e.g., infrared image data)of scene 2170 in accordance with an embodiment of the disclosure.Although infrared image 3000 is depicted as having 16 rows and 16columns, other image sizes are contemplated for infrared image 3000 andthe various other infrared images discussed herein.

In FIG. 23A, infrared image 3000 depicts scene 2170 as relativelyuniform, with a majority of pixels 3010 of infrared image 3000 havingthe same or similar intensity (e.g., the same or similar numbers ofdigital counts). Also in this embodiment, infrared image 3000 includespixels 3020 which are depicted somewhat darker than other pixels 3010 ofinfrared image 3000, and pixels 3030 which are depicted somewhatlighter. As previously mentioned, for purposes of discussion, it will beassumed that darker pixels are associated with higher numbers of digitalcounts, however lighter pixels may be associated with higher numbers ofdigital counts in other implementations if desired.

In some embodiments, infrared image 3000 may be an image frame receivedat block 560 and/or block 565 of FIGS. 5 and 8 previously describedherein. In this regard, infrared image 3000 may be an intentionallyblurred image frame provided by block 555 and/or 560 in which much ofthe high frequency content has already been filtered out due to, forexample, temporal filtering, defocusing, motion, accumulated imageframes, and/or other techniques as appropriate. As such, in someembodiments, any remaining high spatial frequency content (e.g.,exhibited as areas of contrast or differences in the blurred imageframe) remaining in infrared image 3000 may be attributed to spatiallycorrelated FPN and/or spatially uncorrelated FPN.

As such, it can be assumed that substantially uniform pixels 3010generally correspond to blurred scene information, and pixels 3020 and3030 correspond to FPN. For example, as shown in FIG. 23A, pixels 3020and 3030 are arranged in several groups, each of which is positioned ina general area of infrared image 3000 that spans multiple rows andcolumns, but is not correlated to a single row or column.

Various techniques described herein may be used to determine NUC termswithout overcompensating for the presence of nearby dark or lightpixels. As will be further described herein, when such techniques areused to determine NUC terms for individual pixels (e.g., 3040, 3050, and3060) of infrared image 3000, appropriate NUC terms may be determined tocompensate for FPN where appropriate in some cases withoutovercompensating for FPN in other cases.

In accordance with various embodiments further described herein, acorresponding NUC term may be determined for each pixel of an infraredimage. In this regard, a selected pixel of the infrared image may becompared with a corresponding set of other pixels (e.g., also referredto as neighborhood pixels) that are within a neighborhood associatedwith the selected pixel. In some embodiments, the neighborhood maycorrespond to pixels within a selected distance (e.g., within a selectedkernel size) of the selected pixel (e.g., an N by N neighborhood ofpixels around and/or adjacent to the selected pixel). For example, insome embodiments, a kernel of 5 may be used, but larger and smallersizes are also contemplated.

As similarly discussed with regard to FIGS. 19A-22B, one or morecounters (e.g., registers, memory locations, accumulators, and/or otherimplementations in processing component 2110, noise filtering module2112, memory component 2120, and/or other components) are adjusted(e.g., incremented, decremented, or otherwise updated) based on thecomparisons. In this regard, for each comparison where the selectedpixel has a lesser value than a compared pixel of the neighborhood, acounter E may be adjusted. For each comparison where the selected pixelhas an equal (e.g., exactly equal or substantially equal) value as acompared pixel of the neighborhood, a counter F may be adjusted. Foreach comparison where the selected pixel has a greater value than acompared pixel of the neighborhood, a counter G may be adjusted. Thus,if the neighborhood uses a kernel of 5, then a total of 24 comparisonsmay be made between the selected pixel and its neighborhood pixels.Accordingly, a total of 24 adjustments (e.g., counts) may becollectively held by counters E, F, and G. In this regard, counters E,F, and G may identify the number of comparisons for which neighborhoodpixels were greater, equal, or less than the selected pixel.

After the selected pixel has been compared to all pixels in itsneighborhood, a NUC term may be determined (e.g., adjusted) for thepixel based on the values of counters E, F, and G. Based on thedistribution of the counts in counters E, F, and G, the NUC term for theselected pixel may be selectively incremented, decremented, or remainthe same based on one or more calculations performed using values of oneor more of counters E, F, and/or G.

Such adjustment of the NUC term may be performed in accordance with anydesired calculation. For example, in some embodiments, if counter F issignificantly larger than counters E and G or above a particularthreshold value (e.g., indicating that a large number of neighborhoodpixels are exactly equal or substantially equal to the selected pixel),then it may be decided that the NUC term should remain the same. In thiscase, even if several neighborhood pixels exhibit values that aresignificantly higher or lower than the selected pixel, thoseneighborhood pixels will not skew the NUC term as might occur in othermean-based or median-based calculations.

As another example, in some embodiments, if counter E or counter G isabove a particular threshold value (e.g., indicating that a large numberof neighborhood pixels are greater than or less than the selectedpixel), then it may be decided that the NUC term should be incrementedor decremented as appropriate. In this case, because the NUC term may beincremented or decremented based on the number of neighborhood pixelsgreater, equal, or less than the selected pixel (e.g., rather than theactual pixel values of such neighborhood pixels), the NUC term may beadjusted in a gradual fashion without introducing rapid changes that mayinadvertently overcompensate for pixel value differences.

The process may be repeated by resetting counters E, F, and G, selectinganother pixel of infrared image 3000, performing comparisons with itsneighborhood pixels, and determining its NUC term based on the newvalues of counters E, F, and G. These operations can be repeated asdesired until a NUC term has been determined for every pixel of infraredimage 3000.

In some embodiments, after NUC terms have been determined for allpixels, the process may be repeated to further update the NUC termsusing the same infrared image 3000 (e.g., after application of the NUCterms) and/or another infrared image (e.g., a subsequently capturedinfrared image).

As discussed, counters E, F, and G identify the number of neighborhoodpixels that are greater than, equal to, or less than the selected pixel.This contrasts with various other techniques used to determine NUC termswhere the actual differences (e.g., calculated difference values)between compared pixels may be used.

Counters E, F, and G identify relative relationships (e.g., less than,equal to, or greater than relationships) between the selected pixel andits neighborhood pixels. In some embodiments, such relativerelationships may correspond, for example, to the sign (e.g., positive,negative, or zero) of the difference between the values of the selectedpixel and its neighborhood pixels. By determining NUC terms based onrelative relationships rather than actual numerical differences, the NUCterms may not be skewed by small numbers of neighborhood pixels havingdigital counts that widely diverge from the selected pixel.

In addition, using this approach may reduce the effects of other typesof scene information on NUC term values. In this regard, becausecounters E, F, and G identify relative relationships between pixelsrather than actual numerical differences, exponential scene changes(e.g., non-linear scene information gradients) may contribute less toNUC term determinations. For example, exponentially higher digitalcounts in certain pixels may be treated as simply being greater than orless than other pixels for comparison purposes and consequently will notunduly skew the NUC term. Moreover, this approach may be used withoutunintentionally distorting infrared images exhibiting a nonlinear slope.

Advantageously, counters E, F, and G provide an efficient approach tocalculating NUC terms. In this regard, in some embodiments, only threecounters E, F, and G are used to store the results of all neighborhoodpixel comparisons performed for a selected pixel. This contrasts withvarious other approaches in which many more unique values are stored(e.g., where particular numerical differences, or the number ofoccurrences of such numerical differences, are stored), median filtersare used (e.g., which may require sorting and the use of high pass orlow pass filters including a computationally intensive divide operationto obtain a weighted mean of neighbor pixel values).

In some embodiments, where the size of a neighborhood and/or kernel isknown, further efficiency may be achieved by omitting counter E. In thisregard, the total number of counts may be known based on the number ofpixels known to be in the neighborhood. In addition, it may be assumedthat any comparisons that do not result in counter E or counter G beingadjusted will correspond to those comparisons where pixels have equalvalues. Therefore, the value that would have been held by counter F maybe determined from counters E and G (e.g., (number of neighborhoodpixels)−counter E value−counter G value=counter F value).

In some embodiments, only a single counter may be used. In this regard,a single counter may be selectively adjusted in a first manner (e.g.,incremented or decremented) for each comparison where the selected pixelhas a greater value than a neighborhood pixel, selectively adjusted in asecond manner (e.g., decremented or incremented) for each comparisonwhere the selected pixel has a lesser value than a neighborhood pixel,and not adjusted (e.g., retaining its existing value) for eachcomparison where the selected pixel has an equal (e.g., exactly equal orsubstantially equal) value as a neighborhood pixel. Thus, the value ofthe single counter may indicate relative numbers of compared pixels thatare greater than or less than the selected pixel (e.g., after theselected pixel has been compared with all of its correspondingneighborhood pixels).

A NUC term for the selected pixel may be updated (e.g., incremented,decremented, or remain the same) based on the value of the singlecounter. For example, in some embodiments, if the single counterexhibits a baseline value (e.g., zero or other number) after comparisonsare performed, then the NUC term may remain the same. In someembodiments, if the single counter is greater or less than the baselinevalue, the NUC term may be selectively incremented or decremented asappropriate to reduce the overall differences between the selected pixeland the its corresponding neighborhood pixels. In some embodiments, theupdating of the NUC term may be conditioned on the single counter havinga value that differs from the baseline value by at least a thresholdamount to prevent undue skewing of the NUC term based on limited numbersof neighborhood pixels having different values from the selected pixel.

Various aspects of these techniques are further explained with regard toFIGS. 23B-E. In this regard, FIG. 23B is a flowchart illustrating amethod 3100 for noise filtering an infrared image, in accordance with anembodiment of the disclosure. Although particular components of system2100 are referenced in relation to particular blocks of FIG. 23B, thevarious operations described with regard to FIG. 23B may be performed byany appropriate components, such as image capture component 2130,processing component, 2110, noise filtering module 2112, memorycomponent 2120, control component 2140, and/or others. In someembodiments, the operations of FIG. 23B may be performed, for example,in place of blocks 565-573 of FIGS. 5 and 8.

In block 3110, an image frame (e.g., infrared image 3000) is received.For example, as discussed, infrared image 3000 may be an intentionallyblurred image frame provided by block 555 and/or 560.

In block 3120, noise filtering module 2112 selects a pixel of infraredimage 3000 for which a NUC term will be determined. For example, in someembodiments, the selected pixel may be pixel 3040, 3050, or 3060.However, any pixel of infrared image 3000 may be selected. In someembodiments, block 3120 may also include resetting counters E, F, and Gto zero or another appropriate default value.

In block 3130, noise filtering module 2112 selects a neighborhood (e.g.,a pixel neighborhood) associated with the selected pixel. As discussed,in some embodiments, the neighborhood may correspond to pixels within aselected distance of the selected pixel. In the case of selected pixel3040, a kernel of 5 corresponds to a neighborhood 3042 (e.g., including24 neighborhood pixels surrounding selected pixel 3040). In the case ofselected pixel 3050, a kernel of 5 corresponds to a neighborhood 3052(e.g., including 24 neighborhood pixels surrounding selected pixel3050). In the case of selected pixel 3060, a kernel of 5 corresponds toa neighborhood 3062 (e.g., including 24 neighborhood pixels surroundingselected pixel 3060). As discussed, larger and smaller kernel sizes arealso contemplated.

In blocks 3140 and 3150, noise filtering module 2112 compares theselected pixel to its neighborhood pixels and adjusts counters E, F, andG based on the comparisons performed in block 3140. Blocks 3140 and 3150may be performed in any desired combination such that counters E, F, andG may be updated after each comparison and/or after all comparisons havebeen performed.

In the case of selected pixel 3040, FIG. 23C shows the adjusted valuesof counters E, F, and G represented by a histogram 3200 after selectedpixel 3040 has been compared to the pixels of neighborhood 3042.Neighborhood 3042 includes 4 pixels having higher values, 17 pixelshaving equal values, and 3 pixels having lower values than selectedpixel 3040. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23C.

In the case of selected pixel 3050, FIG. 23D shows the adjusted valuesof counters E, F, and G represented by a histogram 3250 after selectedpixel 3050 has been compared to the pixels of neighborhood 3052.Neighborhood 3052 includes 0 pixels having higher values, 6 pixelshaving equal values, and 18 pixels having lower values than selectedpixel 3050. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23D.

In the case of selected pixel 3060, FIG. 23E shows the adjusted valuesof counters E, F, and G represented by a histogram 3290 after selectedpixel 3060 has been compared to the pixels of neighborhood 3062.Neighborhood 3062 includes 19 pixels having higher values, 5 pixelshaving equal values, and 0 pixels having lower values than selectedpixel 3060. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23E.

In block 3160, the NUC term for the selected pixel is updated (e.g.,selectively incremented, decremented, or remain the same) based on thevalues of counters E, F, and G. Such updating may be performed inaccordance with any appropriate calculation using the values of countersE, F, and G.

For example, in the case of selected pixel 3040, counter F in FIG. 23Cindicates that most neighborhood pixels (e.g., 17 neighborhood pixels)have values equal to selected pixel 3040, while counters E and Gindicate that smaller numbers of neighborhood pixels have values greaterthan (e.g., 4 neighborhood pixels) or less than (e.g., 3 neighborhoodpixels) selected pixel 3040. Moreover, the number of neighborhood pixelshaving values greater than and less than selected pixel 3040 are similar(e.g., 4 and 3 neighborhood pixels, respectively). Accordingly, in thiscase, noise filtering module 2112 may choose to keep the NUC term forselected pixel 3040 the same (e.g., unchanged) since a further offset ofselected pixel 3040 would likely introduce additional non-uniformityinto infrared image 3000.

In the case of selected pixel 3050, counter G in FIG. 23D indicates thatmost neighborhood pixels (e.g., 18 neighborhood pixels) have values lessthan selected pixel 3050, while counter F indicates that a smallernumber of neighborhood pixels (e.g., 6 neighborhood pixels) have valuesequal to selected pixel 3050, and counter E indicates that noneighborhood pixels (e.g., 0 neighborhood pixels) have values greaterthan selected pixel 3050. These counter values suggest that selectedpixel 3050 is exhibiting FPN that appears darker than most neighborhoodpixels. Accordingly, in this case, noise filtering module 2112 maychoose to decrement the NUC term for selected pixel 3050 (e.g., tolighten selected pixel 3050) such that it exhibits more uniformity withthe large numbers of neighborhood pixels having lower values.

In the case of selected pixel 3060, counter E in FIG. 23E indicates thatmost neighborhood pixels (e.g., 19 neighborhood pixels) have valuesgreater than selected pixel 3060, while counter F indicates that asmaller number of neighborhood pixels (e.g., 5 neighborhood pixels) havevalues equal to selected pixel 3060, and counter G indicates that noneighborhood pixels (e.g., 0 neighborhood pixels) have values less thanselected pixel 3060. These counter values suggest that selected pixel3060 is exhibiting FPN that appears lighter than most neighborhoodpixels. Accordingly, in this case, noise filtering module 2112 maychoose to increment the NUC term for selected pixel 3060 (e.g., todarken selected pixel 3060) such that it exhibits more uniformity withthe large numbers of neighborhood pixels having higher values.

In block 3160, changes to the NUC term for the selected pixel may bemade incrementally. For example, in some embodiments, the NUC term maybe incremented or decremented by a small amount (e.g., only one orseveral digital counts in some embodiments) in block 3160. Suchincremental changes can prevent large rapid changes in NUC terms thatmay inadvertently introduce undesirable non-uniformities in infraredimage 3000. The process of FIG. 23B may be repeated during eachiteration of FIGS. 5 and 8 (e.g., in place of blocks 565 and/or 570).Therefore, if large changes in the NUC term are required, then the NUCterm may be repeatedly incremented and/or decremented during eachiteration until the NUC value stabilizes (e.g., stays substantially thesame during further iterations). In some embodiments, the block 3160 mayfurther include weighting the updated NUC term based on local gradientsand/or temporal damping as described herein.

At block 3170, if additional pixels of infrared image 3000 remain to beselected, then the process returns to block 3120 wherein blocks3120-3170 are repeated to update the NUC term for another selectedpixel. In this regard, blocks 3120-3170 may be iterated at least oncefor each pixel of infrared image 3000 to update the NUC term for eachpixel (e.g., each pixel of infrared image 3000 may be selected and itscorresponding NUC term may be updated during a corresponding iterationof blocks 3120-3170).

At block 3180, after NUC terms have been updated for all pixels ofinfrared image 3000, the process continues to block 575 of FIGS. 5 and8. Operations of one or more of blocks 565-573 may also be performed inaddition to the process of FIG. 23B.

The process of FIG. 23B may be repeated for each intentionally blurredimage frame provided by block 555 and/or 560. In some embodiments, eachnew image frame received at block 3110 may not differ substantially fromother recently received image frames (e.g., in previous iterations ofthe process of FIG. 23B). This may be due to, for example, asubstantially static scene 2170, a slowing changing scene 2170, temporalfiltering of infrared images, and/or other reasons. In these cases, theaccuracy of NUC terms determined by FIG. 23B may improve as they areselectively incremented, decremented, or remain unchanged in eachiteration of FIG. 23B. As a result, in some embodiments, many of the NUCterms may eventually reach a substantially steady state in which theyremain relatively unchanged after a sufficient number of iterations ofFIG. 23B, and while the image frames do not substantially change.

Other embodiments are also contemplated. For example, block 3160 may berepeated multiple times to update one or more NUC terms using the sameinfrared image for each update. In this regard, after a NUC term isupdated in block 3160 or after multiple NUC terms are updated inadditional iterations of block 3160, the process of FIG. 23B may firstapply the one or more updated NUC terms (e.g., also in block 3160) tothe same infrared image used to determine the updated NUC terms andreturn to block 3120 to iteratively update one or more NUC terms usingthe same infrared image in such embodiments. Such an approach may beused, for example, in offline (non-realtime) processing and/or inrealtime implementations with sufficient processing capabilities.

Any of the various techniques described with regard to FIGS. 23A-E maybe combined where appropriate with the other techniques describedherein. For example, some or all portions of the various techniquesdescribed herein may be combined as desired to perform noise filtering.

Various techniques may be used to identify (e.g., detect, designate, orotherwise classify) anomalous pixels in image frames. In someembodiments, such techniques may be used in combination (e.g., before,after, and/or simultaneous) with other processing described herein.Corrective action may also be performed.

Various types of anomalous pixels may be exhibit different types ofanomalous behavior. For example, if an infrared sensor 132 is becomescompletely non-responsive (e.g., due to a lost electrical connection orotherwise), then an anomalous pixel associated with the infrared sensormay exhibit extreme offset non-uniformity, may exhibit no response tochanges in irradiance, and may always remain non-uniform under changingscene conditions. For example, such a pixel may exhibit a fixed valueregardless of changes in an imaged scene. As such, the pixel may exhibita large difference in value in comparison with other pixels and may beapparent in high contrast scenes.

Another type of anomalous pixel may exhibit a significant offset inrelation to other pixels and may respond to at least some changes inirradiance. Yet another type of anomalous pixel may exhibit intermittentor stepped operation. For example, the pixel may flicker such that it isbi-stable between two significantly different output levels.

The various techniques further described herein may be used to identifyany of these various types of anomalous pixels, as well as other typeswhere appropriate.

FPA-based imaging systems typically include sensors and optics. Forexample, infrared imaging module 100 includes infrared sensors 132(e.g., arranged in an FPA provided by infrared sensor assembly 128) andoptical element 180. As infrared radiation is received from a scene, itpasses through optical element 180 and is received by infrared sensors132. Infrared radiation from any point in the scene may be distributedacross an area of infrared sensor assembly 128 (e.g., across multipleinfrared sensors 132) in a manner that depends on the particularimplementation of optical element 180 and infrared sensor assembly 128.

For example, in some embodiments, such a distribution may be determinedby a Point Spread Function (PSF). In this regard, diffraction through anaperture (e.g., a circular aperture of optical element 180) may operateas a limit on how irradiance from an infinitesimally small point in thescene (e.g., a point source) can be focused. In some embodiments, apoint source may be focused to a spot width (e.g., a diffraction spot)identified by:spot width=2.44*λ*F/#

In the above equation, λ is the wavelength of the radiation being imagedby infrared sensors 132 (e.g., approximately 8 μm to approximately 13 μmin some embodiments) and F/# is the f-number of optical element 180(e.g., approximately 1.0 to approximately 1.4 in some embodiments).

More generally, the energy distribution from the diffraction of a pointsource through a circular aperture of optical element 180 is called theAiry disc and is described by:

${I(\theta)} = {I_{0}( \frac{2\;{J_{1}( {{ka}\;\sin\;\theta} )}}{{ka}\;\sin\;\theta} )}^{2}$

In the above equation, a is the radius of the circular aperture, k isequal to 2π/λ and J₁ is a Bessel function.

FIG. 24 illustrates an Airy disk 4000 and a corresponding plot 4050 ofits intensity versus location on an FPA in accordance with an embodimentof the disclosure. For example, in some embodiments, Airy disk 4000 andplot 4050 may be associated with optical element 180 and infraredsensors 132 of infrared imaging module 100 and/or any of the varioussystems, devices, and/or components described herein.

In FIG. 24, Airy disk 4000 exhibits a width 4010 denoted by first minima4020 in plot 4050 (e.g., which may be determined in some embodimentsusing the spot width equation discussed above). Radiation received froma point source may pass through optical element 180 and be effectivelydefocused by optical element 180 to distribute Airy disk 4000 over width4010. Width 4010 may correspond to multiple infrared sensors 132 ofinfrared sensor assembly 128 (e.g., width 4010 between first minima 4020of Airy disk 400 associated with the point source may be greater than awidth of at least two adjacent ones of infrared sensors 132corresponding to two adjacent pixels).

In some embodiments, in addition to diffraction described above, a pointsource may be further defocused, for example, due to possible non-idealbehavior (e.g., aberrations) in optical element 180, fabrication errors,and/or errors in the focus position of optical element 180 (e.g., thedistance between optical element 180 and infrared sensors 132).

Referring again to the spot width equation discussed above, when thespot width (e.g. point spread function) corresponds to a width that islarger than an individual infrared sensor 132 associated with anindividual pixel, there is a limit to the amount of contrast that mayexist between the individual pixel and its neighbor pixels.

For example, if infrared imaging module 100 is implemented with infraredsensors 132 (e.g., microbolometers) spaced at 17 μm to detect infraredradiation having a wavelength of 10 μm and an f-number of F/1.1, thewidth 4010 of the Airy disk 4000 to the first minima 4020 is:2.44*10 um*1.1=26.8 um

In this example, if an infrared sensor 132 is centered on Airy disk 4000of a point source, then the infrared sensor's associated pixel may havea value corresponding to approximately 75 percent of the total infraredenergy (e.g., irradiance) associated with the point source, and each ofits immediate neighbor pixels may each have values corresponding toapproximately 5 percent of the total infrared energy.

Accordingly, in the above example, no individual pixel will exhibit avalue in response to irradiance (e.g., the value may also referred to asa count or signal level) more than approximately 15 times (e.g., 75percent/5 percent=15) the value of an immediate neighbor pixel (e.g.,immediately adjacent pixel). Thus, in this example, for a point sourcein an imaged scene, values of adjacent pixels are expected to exhibit amaximum ratio of 15 (e.g., also referred to as a factor). In some cases,this ratio may be even smaller when aberrations, fabrication tolerances,defocus, and/or other aspects are included.

In accordance with various techniques further described herein, pixelvalues may be determined and used to identify anomalous pixels. In somecases, anomalous pixels may be individual pixels that exhibit largedisparities (e.g., also referred to as local contrast) in value relativeto their neighbor pixels. In particular, such disparities may exceed themaximum ratio expected of a point source as discussed above.Accordingly, if pixels exhibit a greater disparity (e.g., a greaterratio of values) than that which is theoretically allowable by the PSFcalculations discussed above (e.g., greater than a factor of 15 in theabove example) and/or the specific PSF of a particular optical element(e.g., lens), then it may be determined that such disparate pixel valuesare associated with one or more anomalous pixels.

FIG. 25 illustrates a technique to identify anomalous pixels using a PSFin accordance with an embodiment of the disclosure. In some embodiments,the technique described with regard to FIG. 25 may be particularlyuseful to identify anomalous pixels when low contrast scenes are imaged.

In FIG. 25, pixels 4100A-E of an infrared image are shown (e.g., whichmay be part of any of the various infrared image frames describedherein). In particular, pixels 4100A-E are five pixels of a row orcolumn of an infrared image and correspond in this case to neighborhoodshaving a distance of two pixels on either side of pixel 4100C. Thevalues of pixels 4100A-E exhibit a distribution 4150 and may beevaluated to determine whether pixel 4100C is an anomalous pixel.

As discussed, if a pixel of an infrared image exhibits a disparity fromits neighboring pixels that exceeds a theoretical maximum disparityexpected by PSF calculations, then, in some embodiments, the pixel maybe identified as an anomalous pixel. In accordance with techniquesidentified in FIG. 25, if a selected pixel (e.g., pixel 4100 c) has avalue greater than a threshold value (e.g., a selected threshold valueindicative of anomalous pixel behavior) corresponding to a percentage ofa total (e.g., a sum) of digital counts (e.g., pixel values) associatedwith a set of pixels including the selected pixel and neighborhoodpixels, then the selected pixel may be determined to be anomalous. Sucha process may be repeated for all pixels of an infrared image todetermine whether any of the pixels are anomalous.

In the particular embodiment identified in FIG. 25, pixel 4100C may beconsidered anomalous (e.g., “dead”) with respect to a neighborhoodincluding left side pixels 4100A-B if its value exceeds 90 percent ofthe sum of the value of pixels 4100A-C (e.g., dead_(psl) is true).Similarly, pixel 4100C may be considered anomalous with respect to aneighborhood including right side pixels 4100D-E if its value exceeds 90percent of the sum of the value of pixels 4100C-E (e.g., dead_(psr) istrue). If either case is true, then it may be determined that pixel4100C is exhibiting an anomalous value in relation to neighborhoodpixels 4100A-B and/or 4100D-E, and thus may be identified as ananomalous pixel (e.g., dead_(ps) is true). Although particular sizes andvalues are identified in FIG. 25, other sizes and values may be usedwhere appropriate.

FIG. 26 illustrates a technique to identify anomalous pixels using anintentionally blurred image frame 4210 in accordance with an embodimentof the disclosure. In some embodiments, high contrast edges are blurredin image frame 4210. As a result, the technique described with regard toFIG. 26 may be particularly useful to identify anomalous pixels in someembodiments where high contrast scenes are imaged.

In FIG. 26, blurred image frame 4210 is provided by averaging N imageframes 4200. For example, in some embodiments, blurred image frame 4210may be the image frame provided in block 545 obtained as a result ofaccumulation (block 535) and averaging (block 540) as previouslydiscussed.

Other techniques may be used to provide blurred image frame 4210. Forexample, in some embodiments, blurred image frame 4210 may be the imageframe provided in block 545 obtained as a result of defocusing in block530 as previously discussed. In some embodiments, blurred image frame4210 may be temporally filtered image frame 802 e obtained by temporalfiltering performed in block 826 as previously discussed. It iscontemplated that blurred image frame 4210 may be obtained by othertechniques where appropriate.

In FIG. 26, pixels of blurred image frame 4210 are shown. In particular,a pixel 4220 is shown with neighborhood pixels 4240 in a neighborhood4230 corresponding to a kernel (e.g., a 3 by 3 kernel or any otherappropriate size).

As discussed, if a pixel of an infrared image exhibits a disparity fromits neighboring pixels that exceeds a theoretical maximum disparityexpected by PSF calculations, then, in some embodiments, the pixel maybe identified as an anomalous pixel. In accordance with techniquesidentified in FIG. 26, if a selected pixel (e.g., pixel 4220) differsfrom the average value of a set of neighborhood pixels (e.g., pixels4240) by more than a threshold value (e.g., a selected threshold valueindicative of anomalous pixel behavior), then the selected pixel may bedetermined to be anomalous. Such a process may be repeated for allpixels of an infrared image to determine whether any of the pixels areanomalous.

In the particular example shown in FIG. 26, pixel 4220 may be identifiedas an anomalous pixel (e.g., dead_(ta) is true) if the absolute value ofthe difference between pixel 4220 (e.g., Cp) and the average ofneighborhood pixels 4240 (e.g., nhood_avg) is greater than a thresholdvalue (e.g., 200 in this example). Although particular sizes and valuesare identified in FIG. 26, other sizes and values may be used whereappropriate.

In some embodiments, the techniques described with regard to FIGS. 25and 26 may be selectively performed together or separately to identifyanomalous pixels of image frames.

FIG. 27 is a flowchart 4300 illustrating a process to identify anomalouspixels in accordance with an embodiment of the disclosure. Althoughparticular components are referenced in relation to particular blocks ofFIG. 27, any appropriate components may be used, such as the variouscomponents described herein.

In block 4310, an infrared image frame (e.g., an infrared image) iscaptured by infrared sensors 132. In block 4315, processor 195 performsa contrast determination on the captured image frame. For example,processor 195 may determine whether the captured image is generally alow contrast image or a high contrast image. In this regard, asdiscussed, the technique explained with regard to FIG. 25 may be usefulto identify anomalous pixels when low contrast scenes are imaged, andthe technique explained with regard to FIG. 26 may be useful to identifyanomalous pixels when high contrast scenes are imaged. Accordingly, insome embodiments, the process of FIG. 27 may selectively perform thetechniques of FIGS. 25 and/or 26 based on the low or high contrastdetermination of block 4315. If the captured image is a high contrastimage, the process continues to block 4230. Otherwise, the processcontinues to block 4325.

In block 4320, a blurred image frame is obtained in accordance with thetechnique of FIG. 26, for example, using the various techniquesdescribed herein.

In block 4323, the blurred image frame is optionally high pass filteredand/or otherwise processed to remove pixel value contributionsassociated with background noise. In this regard, in some embodiments,the processing techniques described with reference to FIGS. 24-27 may beperformed based on pixel values relative to background pixel values(e.g., pixel values associated with a substantially uniform scenebackground). For example, background pixel values may be substantiallygreater than zero counts (e.g., as a result of self heating of infraredsensors 132 and/or other causes).

To reduce the contribution of such background pixel values to anomalouspixel determinations, an image frame may be high pass filtered and/orotherwise processed to remove pixel value contributions associated withbackground noise (e.g., in block 4323) before pixel values areprocessed. As a result, anomalous pixel values may be more accuratelydetermined. For example, even if a selected pixel exhibits a relativelysmall difference with respect to compared neighborhood pixels, such adifference may be more pronounced in relation to other pixels (e.g.,those pixels associated with background irradiance) after high passfiltering. In some embodiments, the various threshold values discussedherein may be set or adjusted as desired to identify anomalous pixelsusing post-high pass filtered pixel values.

In block 4325, processor 195 selects a first pixel. If the technique ofFIG. 25 is used (e.g., a low contrast image determination), then theselected pixel may be a pixel of the image frame previously captured inblock 4310. If the technique of FIG. 26 is used (e.g., a high contrastimage determination), then the selected pixel may be a pixel of theblurred image frame obtained in block 4320. One or more additional imageframes may be captured and used for the pixel selection in block 4325,and any single image frame or combination of image frames may be used asappropriate.

In block 4330, processor 195 selects a neighborhood. If the technique ofFIG. 25 is used, then the neighborhood may be, for example, aneighborhood including two pixels on at least one side of the selectedpixel (e.g., pixels 4100A-B and/or pixels 4100D-E if pixel 4100C isselected). If the technique of FIG. 26 is used, then the neighborhoodmay be, for example, a neighborhood determined by a kernel (e.g., pixels4240 if pixel 4220 is selected).

In block 4335, processor 195 performs calculations based on pixel valuesof the selected pixel and the neighborhood pixels. If the technique ofFIG. 25 is used, then processor 195 may calculate a percentage of thesum of the values of pixels 4100A-C and/or 4100C-D. If the technique ofFIG. 26 is used, then processor 195 may calculate an average value ofpixels 4240 and the absolute difference between the average value andthe value of pixel 4220.

In block 4340, processor 195 determines whether a threshold has been met(e.g., exceeded in some embodiments). If the technique of FIG. 25 isused, then processor 195 may use the result determined in block 4335 asa threshold value and compare it with the value of pixel 4100C. If thetechnique of FIG. 26 is used, then processor 195 may compare theabsolute difference determined in block 4335 with a threshold value. Ifthe threshold has been met, the process continues to block 4350.Otherwise, the process continues to block 4345.

In block 4345, processor 195 determines whether one or more additionalneighborhoods remain to be evaluated for the selected pixel. In thisregard, it may be desirable in some embodiments to evaluate additionalneighborhoods before making a determination regarding whether theselected pixel is anomalous. If additional neighborhoods remain, thenthe process returns to block 4330. Otherwise, the process continues toblock 4355.

For example, if the technique of FIG. 25 is used with a neighborhoodincluding pixels 4100A-B during an iteration of blocks 4330-4340, thenthe process may return to block 4330 to use a different neighborhoodincluding pixels 4100D-E. As another example, if the technique of FIG.25 is used with a neighborhood including only a single row or a singlecolumn during an iteration of blocks 4330-4340, then the process mayreturn to block 4330 to use a column instead of a row, or vice versa. Asanother example, if the technique of FIG. 26 is used with a neighborhoodusing a particular kernel during an iteration of blocks 4330-4340, thenthe process may return to block 4330 to use a different neighborhoodhaving a different kernel.

Referring again to block 4340, if the threshold was met in previousblock 4340, then the selected pixel will have satisfied at least apreliminary condition to be designated as anomalous. In block 4350, oneor more additional criteria may be evaluated to further determinewhether the selected pixel should be identified as an anomalous pixel.In various embodiments, the criteria of block 4340 may be evaluatedbefore, after, or during the other operations of FIG. 27.

In some embodiments, block 4340 may include processor 195 executinginstructions (e.g., conditional logic instructions) to prevent theselected pixel from being identified as anomalous if such a designationwould create a cluster of anomalous pixels greater than a desired size(e.g., to ensure reliable operation of corrective action such as pixelreplacement operations).

In some embodiments, block 4340 may include processor 195 executinginstructions to evaluate pixels value of the selected pixel and/orneighborhood pixels in relation to background noise levels. For example,in some embodiments, the selected pixel may be identified asnon-anomalous if its value is within (e.g., less than) a temporal noisethreshold (e.g., within an 8 times standard deviation relative tobackground noise levels).

If the criteria of block 4350 is met, then the process continues toblock 4355. Otherwise, the process continues to block 4360.

In block 4355, processor 195 designates (e.g., identifies) the selectedpixel as an anomalous pixel. For example, in some embodiments, block4355 may include updating a bad pixel map (e.g., stored in anappropriate memory or other machine-readable medium) to identify theselected pixel as anomalous.

In block 4360, if additional pixels of the captured image frame remainto be evaluated, then the process returns to block 4325 where anotherpixel of the image frame is selected. Otherwise, the process continuesto block 4365.

In block 4365, corrective action is taken for any anomalous pixels thathave been identified (e.g., designated). In some embodiments, suchcorrective action may include substituting other values for theanomalous pixels (e.g., pixel replacement), performing any of thevarious processes discussed herein to reduce noise and/or othernon-uniformities (e.g., to reduce or eliminate effects of the anomalouspixels), other corrective action (e.g., determination and application ofrow and/or column correction terms using various techniques describedherein), and/or various combinations of such actions as appropriate.

In some embodiments, one or more blocks of the process of FIG. 27 may berepeated in an iterative fashion on the same or different image framesto continue to identify anomalous pixels (e.g., continuing to update abad pixel map). For example, in some embodiments, additional anomalouspixels (e.g., clusters of anomalous pixels) may be determined as theprocess of FIG. 27 iterates. In some embodiments, corrective actiontaken in block 4365 (e.g., pixel replacement) may be performed for alimited period of time and/or until particular pixels are no longeridentified as anomalous during further iterations of the process of FIG.27. Moreover, selected pixels may be selectively designated as anomalousor non-anomalous during various iterations of one or more blocks of theprocess of FIG. 27 (e.g., a selected pixel may transition between beingdesignated as anomalous and non-anomalous during various iterations).

Advantageously, the various techniques described with regard to FIGS.24-27 may be performed in the field in response to automated or manualtriggering after an imaging device has shipped from the factory. As aresult, anomalous pixels that were not identified during manufacture, orthat may begin to exhibit anomalous behavior after shipping, may beidentified and corrected in the field (e.g., during use of the imagingdevice).

The various techniques described with regard to FIGS. 24-27 permit manytypes of anomalous pixels to be identified and corrected. For example,pixels associated with completely non-responsive infrared sensors 132may be identified and corrected.

As another example, pixels that exhibit significant offsets but thatalso respond to at least some changes infrared sensor signals may beidentified and corrected. In some embodiments, such pixels may becorrected by continual replacement. In other embodiments, such pixelsmay be initially replaced and subsequently corrected using variousnon-uniformity techniques such as, for example, those described herein.

As another example, flickering pixels may be identified and corrected.In some embodiments, by iteratively performing various techniques ofFIGS. 24-27, such pixels may be rapidly identified as anomalous andcorrected after they transition to an uncorrected value, and rapidlyidentified as non-anomalous and left uncorrected after they transitionto a normal expected value.

Any of the various methods, processes, and/or operations describedherein may be performed by any of the various systems, devices, and/orcomponents described herein where appropriate. Moreover, althoughvarious methods, processes, and/or operations described herein have beendiscussed with regard to infrared images, such techniques may be appliedto other images (e.g., visible spectrum images and/or other spectra)where appropriate.

Where applicable, various embodiments provided by the present disclosurecan be implemented using hardware, software, or combinations of hardwareand software. Also where applicable, the various hardware componentsand/or software components set forth herein can be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein can be separated into sub-components comprising software,hardware, or both without departing from the spirit of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components can be implemented as hardware components, andvice-versa.

Software in accordance with the present disclosure, such asnon-transitory instructions, program code, and/or data, can be stored onone or more non-transitory machine readable mediums. It is alsocontemplated that software identified herein can be implemented usingone or more general purpose or specific purpose computers and/orcomputer systems, networked and/or otherwise. Where applicable, theordering of various steps described herein can be changed, combined intocomposite steps, and/or separated into sub-steps to provide featuresdescribed herein.

Embodiments described above illustrate but do not limit the invention.It should also be understood that numerous modifications and variationsare possible in accordance with the principles of the invention.Accordingly, the scope of the invention is defined only by the followingclaims.

What is claimed is:
 1. A method comprising: receiving an infrared imageframe captured by a plurality of infrared sensors based on infraredradiation passed through an optical element configured to exhibit anAiry disk diffraction pattern in response to a point source, wherein awidth between minima of the Airy disk is greater than a width of atleast two adjacent ones of the infrared sensors; selecting a first pixelof the infrared image frame; selecting a second pixel of the infraredimage frame adjacent to the first pixel; processing values of theselected first pixel and the selected second pixel to determine whethera ratio of the values of the selected first and second pixels exceeds amaximum ratio associated with the configuration of the optical elementand the infrared sensors; and selectively designating the selected firstpixel as an anomalous pixel based on the processing.
 2. The method ofclaim 1, further comprising selecting a plurality of neighborhood pixelsof the infrared image frame, wherein the processing further comprisesdetermining whether the value of the selected first pixel exceeds athreshold value comprising a percentage of a sum of the values of theselected first pixel and the neighborhood pixels.
 3. The method of claim1, further comprising selecting a plurality of neighborhood pixels ofthe infrared image frame, wherein the processing further comprisesdetermining whether an absolute difference between the value of theselected first pixel and an average of the values of the neighborhoodpixels exceeds a threshold value.
 4. The method of claim 1, furthercomprising high pass filtering the infrared image frame prior to theprocessing.
 5. The method of claim 1, wherein the selected first pixelis not designated as an anomalous pixel if the value of the selectedfirst pixel is less than a background noise threshold.
 6. The method ofclaim 1, further comprising, if the selected first pixel is designatedas an anomalous pixel, identifying the selected first pixel in a badpixel map.
 7. The method of claim 1, wherein the infrared image frame isa first infrared image frame, wherein the selected first pixel isdesignated as an anomalous pixel during a first iteration of the method,the method further comprising: performing a second iteration of themethod using a second infrared image frame; and designating the selectedfirst pixel as a non-anomalous pixel based on the second iteration ofthe processing.
 8. The method of claim 1, further comprising, if theselected first pixel is designated as an anomalous pixel, correcting thevalue of the selected first pixel.
 9. The method of claim 8, wherein thecorrecting comprises determining a non-uniformity correction (NUC) termassociated with the selected first pixel.
 10. The method of claim 9,wherein the infrared image frame is an intentionally blurred imageframe.
 11. The method of claim 1, further comprising processing theinfrared image frame to determine a plurality of column correction termsto reduce noise introduced by an infrared imaging device, wherein eachcolumn correction term is associated with a corresponding column of theinfrared image frame and is determined based on relative relationshipsbetween pixels of the corresponding column and pixels of a neighborhoodof columns.
 12. The method of claim 1, wherein the infrared image frameis a thermal image frame.
 13. The method of claim 1, further comprising:passing the infrared radiation through the optical element; andcapturing the infrared image frame using the infrared sensors.
 14. Asystem comprising: a memory adapted to receive an infrared image framecaptured by a plurality of infrared sensors based on infrared radiationpassed through an optical element configured to exhibit an Airy diskdiffraction pattern in response to a point source, wherein a widthbetween minima of the Airy disk is greater than a width of at least twoadjacent ones of the infrared sensors; and a processor adapted toexecute instructions to: select a first pixel of the infrared imageframe, select a second pixel of the infrared image frame adjacent to thefirst pixel, process values of the selected first pixel and the selectedsecond pixel to determine whether a ratio of the values of the selectedfirst and second pixels exceeds a maximum ratio associated with theconfiguration of the optical element and the infrared sensors, andselectively designate the selected first pixel as an anomalous pixelbased on the process.
 15. The system of claim 14, further comprising aplurality of neighborhood pixels, wherein the instructions to processthe values of the selected first pixel and the neighborhood pixels areadapted to cause the processor to determine whether the value of theselected first pixel exceeds a threshold value comprising a percentageof a sum of the values of the selected first pixel and the neighborhoodpixels.
 16. The system of claim 14, further comprising a plurality ofneighborhood pixels, wherein the instructions to process the values ofthe selected first pixel and the neighborhood pixels are adapted tocause the processor to determine whether an absolute difference betweenthe value of the selected first pixel and an average of the values ofthe neighborhood pixels exceeds a threshold value.
 17. The system ofclaim 14, wherein the processor is adapted to execute instructions tohigh pass filter the infrared image frame prior to the process.
 18. Thesystem of claim 14, wherein the selected first pixel is not designatedas an anomalous pixel if the value of the selected first pixel is lessthan a background noise threshold.
 19. The system of claim 14, whereinthe processor is adapted to execute instructions to, if the selectedfirst pixel is designated as an anomalous pixel, identify the selectedfirst pixel in a bad pixel map.
 20. The system of claim 14, wherein theinfrared image frame is a first infrared image frame, wherein theselected first pixel is designated as an anomalous pixel during a firstexecution of the instructions, wherein the processor is adapted to:perform a second execution of the instructions using a second infraredimage frame; and execute additional instructions to designate theselected first pixel as a non-anomalous pixel based on the secondexecution of the process.
 21. The system of claim 14, wherein theprocessor is adapted to execute additional instructions to, if theselected first pixel is designated as an anomalous pixel, correct thevalue of the selected first pixel.
 22. The system of claim 21, whereinthe instructions to correct the value of the selected first pixel areadapted to cause the processor to determine a non-uniformity correction(NUC) term associated with the selected first pixel.
 23. The system ofclaim 22, wherein the infrared image frame is an intentionally blurredimage frame.
 24. The system of claim 14, wherein the processor isadapted to execute additional instructions to process the infrared imageframe to determine a plurality of column correction terms to reducenoise introduced by an infrared imaging device, wherein each columncorrection term is associated with a corresponding column of theinfrared image frame and is determined based on relative relationshipsbetween pixels of the corresponding column and pixels of a neighborhoodof columns.
 25. The system of claim 14, wherein the infrared image frameis a thermal image frame.
 26. The system of claim 14, furthercomprising: the optical element; and the infrared sensors.
 27. Thesystem of claim 26, wherein the infrared sensors are microbolometersadapted to receive a bias voltage selected from a range of approximately0.2 to approximately 0.7 volts.